id	sid	tid	token	lemma	pos
fcis-10203	1	1	frontiers	frontier	NOUN
fcis-10203	1	2	in	in	ADP
fcis-10203	1	3	computing	computing	NOUN
fcis-10203	1	4	and	and	CCONJ
fcis-10203	1	5	intelligent	intelligent	ADJ
fcis-10203	1	6	systems	system	NOUN
fcis-10203	1	7	issn	issn	VERB
fcis-10203	1	8	:	:	PUNCT
fcis-10203	1	9	2832	2832	NUM
fcis-10203	1	10	-	-	SYM
fcis-10203	1	11	6024	6024	NUM
fcis-10203	1	12	|	|	NOUN
fcis-10203	1	13	vol	vol	NOUN
fcis-10203	1	14	.	.	PROPN
fcis-10203	2	1	4	4	NUM
fcis-10203	2	2	,	,	PUNCT
fcis-10203	2	3	no	no	INTJ
fcis-10203	2	4	.	.	NOUN
fcis-10203	2	5	2	2	NUM
fcis-10203	2	6	,	,	PUNCT
fcis-10203	2	7	2023	2023	NUM
fcis-10203	2	8	63	63	NUM
fcis-10203	2	9	a	a	DET
fcis-10203	2	10	question	question	NOUN
fcis-10203	2	11	answering	answer	VERB
fcis-10203	2	12	system	system	NOUN
fcis-10203	2	13	for	for	ADP
fcis-10203	2	14	situation	situation	NOUN
fcis-10203	2	15	puzzle	puzzle	NOUN
fcis-10203	2	16	with	with	ADP
fcis-10203	2	17	spqa	spqa	PROPN
fcis-10203	2	18	tian	tian	ADJ
fcis-10203	2	19	zhao	zhao	PROPN
fcis-10203	2	20	*	*	PUNCT
fcis-10203	2	21	mathematics	mathematics	PROPN
fcis-10203	2	22	department	department	PROPN
fcis-10203	2	23	,	,	PUNCT
fcis-10203	2	24	city	city	PROPN
fcis-10203	2	25	university	university	PROPN
fcis-10203	2	26	of	of	ADP
fcis-10203	2	27	hong	hong	PROPN
fcis-10203	2	28	kong	kong	PROPN
fcis-10203	2	29	,	,	PUNCT
fcis-10203	2	30	hong	hong	PROPN
fcis-10203	2	31	kong	kong	PROPN
fcis-10203	2	32	sar	sar	PROPN
fcis-10203	2	33	,	,	PUNCT
fcis-10203	2	34	999077	999077	NUM
fcis-10203	2	35	,	,	PUNCT
fcis-10203	2	36	china	china	PROPN
fcis-10203	2	37	*	*	PUNCT
fcis-10203	2	38	corresponding	correspond	VERB
fcis-10203	2	39	author	author	NOUN
fcis-10203	2	40	email	email	NOUN
fcis-10203	2	41	:	:	PUNCT
fcis-10203	2	42	zhaotian8858@gmail.com	zhaotian8858@gmail.com	X
fcis-10203	3	1	abstract	abstract	NOUN
fcis-10203	3	2	:	:	PUNCT
fcis-10203	3	3	there	there	PRON
fcis-10203	3	4	are	be	VERB
fcis-10203	3	5	many	many	ADJ
fcis-10203	3	6	questions	question	NOUN
fcis-10203	3	7	answering	answer	VERB
fcis-10203	3	8	(	(	PUNCT
fcis-10203	3	9	qa	qa	NOUN
fcis-10203	3	10	)	)	PUNCT
fcis-10203	3	11	system	system	NOUN
fcis-10203	3	12	built	build	VERB
fcis-10203	3	13	for	for	ADP
fcis-10203	3	14	solving	solve	VERB
fcis-10203	3	15	qa	qa	PROPN
fcis-10203	3	16	tasks	task	NOUN
fcis-10203	3	17	.	.	PUNCT
fcis-10203	4	1	in	in	ADP
fcis-10203	4	2	2020	2020	NUM
fcis-10203	4	3	and	and	CCONJ
fcis-10203	4	4	2022	2022	NUM
fcis-10203	4	5	,	,	PUNCT
fcis-10203	4	6	allen	allen	PROPN
fcis-10203	4	7	institute	institute	PROPN
fcis-10203	4	8	and	and	CCONJ
fcis-10203	4	9	the	the	DET
fcis-10203	4	10	university	university	PROPN
fcis-10203	4	11	of	of	ADP
fcis-10203	4	12	washington	washington	PROPN
fcis-10203	4	13	proposed	propose	VERB
fcis-10203	4	14	unifiedqa	unifiedqa	NOUN
fcis-10203	4	15	and	and	CCONJ
fcis-10203	4	16	unifiedqa	unifiedqa	NOUN
fcis-10203	4	17	-	-	PUNCT
fcis-10203	4	18	v2	v2	NOUN
fcis-10203	4	19	.	.	PUNCT
fcis-10203	5	1	their	their	PRON
fcis-10203	5	2	core	core	NOUN
fcis-10203	5	3	concept	concept	NOUN
fcis-10203	5	4	is	be	AUX
fcis-10203	5	5	that	that	SCONJ
fcis-10203	5	6	the	the	DET
fcis-10203	5	7	semantic	semantic	ADJ
fcis-10203	5	8	understanding	understanding	NOUN
fcis-10203	5	9	and	and	CCONJ
fcis-10203	5	10	reasoning	reasoning	NOUN
fcis-10203	5	11	capabilities	capability	NOUN
fcis-10203	5	12	required	require	VERB
fcis-10203	5	13	by	by	ADP
fcis-10203	5	14	models	model	NOUN
fcis-10203	5	15	are	be	AUX
fcis-10203	5	16	common	common	ADJ
fcis-10203	5	17	,	,	PUNCT
fcis-10203	5	18	and	and	CCONJ
fcis-10203	5	19	may	may	AUX
fcis-10203	5	20	not	not	PART
fcis-10203	5	21	require	require	VERB
fcis-10203	5	22	format	format	NOUN
fcis-10203	5	23	specific	specific	ADJ
fcis-10203	5	24	models	model	NOUN
fcis-10203	5	25	although	although	SCONJ
fcis-10203	5	26	the	the	DET
fcis-10203	5	27	qa	qa	PROPN
fcis-10203	5	28	task	task	NOUN
fcis-10203	5	29	forms	form	NOUN
fcis-10203	5	30	are	be	AUX
fcis-10203	5	31	different	different	ADJ
fcis-10203	5	32	.	.	PUNCT
fcis-10203	6	1	behind	behind	ADP
fcis-10203	6	2	this	this	DET
fcis-10203	6	3	concept	concept	NOUN
fcis-10203	6	4	,	,	PUNCT
fcis-10203	6	5	i	i	PRON
fcis-10203	6	6	build	build	VERB
fcis-10203	6	7	a	a	DET
fcis-10203	6	8	new	new	ADJ
fcis-10203	6	9	qa	qa	PROPN
fcis-10203	6	10	model	model	NOUN
fcis-10203	6	11	named	name	VERB
fcis-10203	6	12	spqa	spqa	ADJ
fcis-10203	6	13	,	,	PUNCT
fcis-10203	6	14	aiming	aim	VERB
fcis-10203	6	15	to	to	PART
fcis-10203	6	16	answer	answer	VERB
fcis-10203	6	17	the	the	DET
fcis-10203	6	18	situation	situation	NOUN
fcis-10203	6	19	puzzle	puzzle	NOUN
fcis-10203	6	20	questions	question	NOUN
fcis-10203	6	21	by	by	ADP
fcis-10203	6	22	adding	add	VERB
fcis-10203	6	23	new	new	ADJ
fcis-10203	6	24	situation	situation	NOUN
fcis-10203	6	25	-	-	PUNCT
fcis-10203	6	26	puzzle	puzzle	NOUN
fcis-10203	6	27	related	relate	VERB
fcis-10203	6	28	dataset	dataset	NOUN
fcis-10203	6	29	(	(	PUNCT
fcis-10203	6	30	spq	spq	NOUN
fcis-10203	6	31	)	)	PUNCT
fcis-10203	6	32	.	.	PUNCT
fcis-10203	7	1	in	in	ADP
fcis-10203	7	2	addition	addition	NOUN
fcis-10203	7	3	,	,	PUNCT
fcis-10203	7	4	i	i	PRON
fcis-10203	7	5	evaluate	evaluate	VERB
fcis-10203	7	6	the	the	DET
fcis-10203	7	7	performance	performance	NOUN
fcis-10203	7	8	of	of	ADP
fcis-10203	7	9	spqa	spqa	NOUN
fcis-10203	7	10	and	and	CCONJ
fcis-10203	7	11	unifiedqa	unifiedqa	NOUN
fcis-10203	7	12	-	-	PUNCT
fcis-10203	7	13	v2	v2	NOUN
fcis-10203	7	14	for	for	ADP
fcis-10203	7	15	fine	fine	ADV
fcis-10203	7	16	-	-	PUNCT
fcis-10203	7	17	tuning	tuning	NOUN
fcis-10203	7	18	and	and	CCONJ
fcis-10203	7	19	prompt	prompt	NOUN
fcis-10203	7	20	-	-	PUNCT
fcis-10203	7	21	tuning	tuning	NOUN
fcis-10203	7	22	.	.	PUNCT
fcis-10203	8	1	the	the	DET
fcis-10203	8	2	results	result	NOUN
fcis-10203	8	3	of	of	ADP
fcis-10203	8	4	fine	fine	ADV
fcis-10203	8	5	-	-	PUNCT
fcis-10203	8	6	tuning	tuning	NOUN
fcis-10203	8	7	indicate	indicate	VERB
fcis-10203	8	8	that	that	DET
fcis-10203	8	9	spq	spq	NOUN
fcis-10203	8	10	dataset	dataset	NOUN
fcis-10203	8	11	is	be	AUX
fcis-10203	8	12	important	important	ADJ
fcis-10203	8	13	for	for	ADP
fcis-10203	8	14	fine	fine	ADV
fcis-10203	8	15	-	-	PUNCT
fcis-10203	8	16	tuning	tuning	NOUN
fcis-10203	8	17	and	and	CCONJ
fcis-10203	8	18	prompttuning	prompttune	VERB
fcis-10203	8	19	to	to	PART
fcis-10203	8	20	answer	answer	VERB
fcis-10203	8	21	situation	situation	NOUN
fcis-10203	8	22	puzzle	puzzle	NOUN
fcis-10203	8	23	questions	question	NOUN
fcis-10203	8	24	well	well	ADV
fcis-10203	8	25	,	,	PUNCT
fcis-10203	8	26	but	but	CCONJ
fcis-10203	8	27	also	also	ADV
fcis-10203	8	28	make	make	VERB
fcis-10203	8	29	the	the	DET
fcis-10203	8	30	answering	answering	NOUN
fcis-10203	8	31	ability	ability	NOUN
fcis-10203	8	32	of	of	ADP
fcis-10203	8	33	normal	normal	ADJ
fcis-10203	8	34	yes	yes	INTJ
fcis-10203	8	35	/	/	SYM
fcis-10203	8	36	no	no	DET
fcis-10203	8	37	questions	question	NOUN
fcis-10203	8	38	worse	bad	ADJ
fcis-10203	8	39	.	.	PUNCT
fcis-10203	9	1	eventually	eventually	ADV
fcis-10203	9	2	,	,	PUNCT
fcis-10203	9	3	the	the	DET
fcis-10203	9	4	results	result	NOUN
fcis-10203	9	5	of	of	ADP
fcis-10203	9	6	prompt	prompt	NOUN
fcis-10203	9	7	-	-	PUNCT
fcis-10203	9	8	tuning	tuning	NOUN
fcis-10203	9	9	indicate	indicate	VERB
fcis-10203	9	10	that	that	SCONJ
fcis-10203	9	11	the	the	DET
fcis-10203	9	12	effects	effect	NOUN
fcis-10203	9	13	of	of	ADP
fcis-10203	9	14	spq	spq	NOUN
fcis-10203	9	15	is	be	AUX
fcis-10203	9	16	larger	large	ADJ
fcis-10203	9	17	and	and	CCONJ
fcis-10203	9	18	more	more	ADV
fcis-10203	9	19	significant	significant	ADJ
fcis-10203	9	20	on	on	ADP
fcis-10203	9	21	situation	situation	NOUN
fcis-10203	9	22	puzzle	puzzle	NOUN
fcis-10203	9	23	questions	question	NOUN
fcis-10203	9	24	and	and	CCONJ
fcis-10203	9	25	normal	normal	ADJ
fcis-10203	10	1	yes	yes	INTJ
fcis-10203	10	2	/	/	SYM
fcis-10203	10	3	no	no	DET
fcis-10203	10	4	questions	question	NOUN
fcis-10203	10	5	under	under	ADP
fcis-10203	10	6	the	the	DET
fcis-10203	10	7	same	same	ADJ
fcis-10203	10	8	data	data	NOUN
fcis-10203	10	9	scale	scale	NOUN
fcis-10203	10	10	.	.	PUNCT
fcis-10203	11	1	in	in	ADP
fcis-10203	11	2	the	the	DET
fcis-10203	11	3	future	future	ADJ
fcis-10203	11	4	work	work	NOUN
fcis-10203	11	5	,	,	PUNCT
fcis-10203	11	6	the	the	DET
fcis-10203	11	7	further	further	ADJ
fcis-10203	11	8	research	research	NOUN
fcis-10203	11	9	like	like	ADP
fcis-10203	11	10	building	build	VERB
fcis-10203	11	11	larger	large	ADJ
fcis-10203	11	12	spq	spq	NOUN
fcis-10203	11	13	dataset	dataset	NOUN
fcis-10203	11	14	should	should	AUX
fcis-10203	11	15	be	be	AUX
fcis-10203	11	16	considered	consider	VERB
fcis-10203	11	17	.	.	PUNCT
fcis-10203	12	1	keywords	keyword	NOUN
fcis-10203	12	2	:	:	PUNCT
fcis-10203	12	3	nlp	nlp	NOUN
fcis-10203	12	4	;	;	PUNCT
fcis-10203	12	5	question	question	NOUN
fcis-10203	12	6	answering	answering	NOUN
fcis-10203	12	7	;	;	PUNCT
fcis-10203	12	8	situation	situation	NOUN
fcis-10203	12	9	puzzle	puzzle	NOUN
fcis-10203	12	10	;	;	PUNCT
fcis-10203	12	11	unifiedqa	unifiedqa	ADJ
fcis-10203	12	12	;	;	PUNCT
fcis-10203	12	13	unifiedqa	unifiedqa	ADV
fcis-10203	12	14	-	-	PUNCT
fcis-10203	12	15	v2	v2	NOUN
fcis-10203	12	16	;	;	PUNCT
fcis-10203	12	17	spqa	spqa	ADJ
fcis-10203	12	18	.	.	PUNCT
fcis-10203	13	1	1	1	X
fcis-10203	13	2	.	.	X
fcis-10203	13	3	introduction	introduction	NOUN
fcis-10203	13	4	situation	situation	NOUN
fcis-10203	13	5	puzzles	puzzle	NOUN
fcis-10203	13	6	(	(	PUNCT
fcis-10203	13	7	also	also	ADV
fcis-10203	13	8	called	call	VERB
fcis-10203	13	9	“	"	PUNCT
fcis-10203	13	10	lateral	lateral	ADJ
fcis-10203	13	11	thinking	thinking	NOUN
fcis-10203	13	12	puzzles	puzzle	NOUN
fcis-10203	13	13	”	"	PUNCT
fcis-10203	13	14	or	or	CCONJ
fcis-10203	13	15	"	"	PUNCT
fcis-10203	13	16	yes	yes	INTJ
fcis-10203	13	17	/	/	SYM
fcis-10203	13	18	no	no	PRON
fcis-10203	13	19	puzzles	puzzle	NOUN
fcis-10203	13	20	”	"	PUNCT
fcis-10203	13	21	)	)	PUNCT
fcis-10203	13	22	are	be	AUX
fcis-10203	13	23	usually	usually	ADV
fcis-10203	13	24	played	play	VERB
fcis-10203	13	25	by	by	ADP
fcis-10203	13	26	a	a	DET
fcis-10203	13	27	group	group	NOUN
fcis-10203	13	28	of	of	ADP
fcis-10203	13	29	players	player	NOUN
fcis-10203	13	30	.	.	PUNCT
fcis-10203	14	1	the	the	DET
fcis-10203	14	2	players	player	NOUN
fcis-10203	14	3	asking	ask	VERB
fcis-10203	14	4	questions	question	NOUN
fcis-10203	14	5	which	which	PRON
fcis-10203	14	6	can	can	AUX
fcis-10203	14	7	only	only	ADV
fcis-10203	14	8	be	be	AUX
fcis-10203	14	9	answered	answer	VERB
fcis-10203	14	10	with	with	ADP
fcis-10203	14	11	"	"	PUNCT
fcis-10203	14	12	yes	yes	INTJ
fcis-10203	14	13	"	"	PUNCT
fcis-10203	14	14	or	or	CCONJ
fcis-10203	14	15	"	"	PUNCT
fcis-10203	14	16	no	no	NOUN
fcis-10203	14	17	"	"	PUNCT
fcis-10203	14	18	to	to	ADP
fcis-10203	14	19	the	the	DET
fcis-10203	14	20	person	person	NOUN
fcis-10203	14	21	who	who	PRON
fcis-10203	14	22	is	be	AUX
fcis-10203	14	23	hosting	host	VERB
fcis-10203	14	24	the	the	DET
fcis-10203	14	25	game	game	NOUN
fcis-10203	14	26	.	.	PUNCT
fcis-10203	15	1	depending	depend	VERB
fcis-10203	15	2	on	on	ADP
fcis-10203	15	3	the	the	DET
fcis-10203	15	4	settings	setting	NOUN
fcis-10203	15	5	and	and	CCONJ
fcis-10203	15	6	difficulty	difficulty	NOUN
fcis-10203	15	7	of	of	ADP
fcis-10203	15	8	the	the	DET
fcis-10203	15	9	puzzle	puzzle	NOUN
fcis-10203	15	10	,	,	PUNCT
fcis-10203	15	11	some	some	DET
fcis-10203	15	12	information	information	NOUN
fcis-10203	15	13	can	can	AUX
fcis-10203	15	14	be	be	AUX
fcis-10203	15	15	added	add	VERB
fcis-10203	15	16	in	in	ADP
fcis-10203	15	17	the	the	DET
fcis-10203	15	18	answers	answer	NOUN
fcis-10203	15	19	,	,	PUNCT
fcis-10203	15	20	such	such	ADJ
fcis-10203	15	21	as	as	ADP
fcis-10203	15	22	hints	hint	NOUN
fcis-10203	15	23	,	,	PUNCT
fcis-10203	15	24	simple	simple	ADJ
fcis-10203	15	25	explanations	explanation	NOUN
fcis-10203	15	26	about	about	ADP
fcis-10203	15	27	why	why	SCONJ
fcis-10203	15	28	the	the	DET
fcis-10203	15	29	answer	answer	NOUN
fcis-10203	15	30	is	be	AUX
fcis-10203	15	31	that	that	SCONJ
fcis-10203	15	32	,	,	PUNCT
fcis-10203	15	33	or	or	CCONJ
fcis-10203	15	34	be	be	AUX
fcis-10203	15	35	informed	inform	VERB
fcis-10203	15	36	by	by	ADP
fcis-10203	15	37	“	"	PUNCT
fcis-10203	15	38	not	not	PART
fcis-10203	15	39	related	relate	VERB
fcis-10203	15	40	”	"	PUNCT
fcis-10203	15	41	.	.	PUNCT
fcis-10203	16	1	the	the	DET
fcis-10203	16	2	puzzle	puzzle	NOUN
fcis-10203	16	3	is	be	AUX
fcis-10203	16	4	informed	inform	VERB
fcis-10203	16	5	by	by	ADP
fcis-10203	16	6	“	"	PUNCT
fcis-10203	16	7	solved	solve	VERB
fcis-10203	16	8	”	"	PUNCT
fcis-10203	16	9	when	when	SCONJ
fcis-10203	16	10	one	one	NUM
fcis-10203	16	11	of	of	ADP
fcis-10203	16	12	the	the	DET
fcis-10203	16	13	players	player	NOUN
fcis-10203	16	14	can	can	AUX
fcis-10203	16	15	state	state	VERB
fcis-10203	16	16	the	the	DET
fcis-10203	16	17	same	same	ADJ
fcis-10203	16	18	process	process	NOUN
fcis-10203	16	19	or	or	CCONJ
fcis-10203	16	20	truth	truth	NOUN
fcis-10203	16	21	as	as	SCONJ
fcis-10203	16	22	the	the	DET
fcis-10203	16	23	host	host	NOUN
fcis-10203	16	24	’s	’s	PART
fcis-10203	16	25	thought	think	VERB
fcis-10203	17	1	[	[	X
fcis-10203	17	2	1	1	NUM
fcis-10203	17	3	]	]	PUNCT
fcis-10203	17	4	.	.	PUNCT
fcis-10203	18	1	1.1	1.1	NUM
fcis-10203	18	2	.	.	PUNCT
fcis-10203	18	3	background	background	NOUN
fcis-10203	18	4	in	in	ADP
fcis-10203	18	5	2017	2017	NUM
fcis-10203	18	6	,	,	PUNCT
fcis-10203	18	7	transformer	transformer	NOUN
fcis-10203	18	8	(	(	PUNCT
fcis-10203	18	9	vaswani	vaswani	X
fcis-10203	18	10	et	et	PROPN
fcis-10203	18	11	al	al	PROPN
fcis-10203	18	12	.	.	PROPN
fcis-10203	18	13	)	)	PUNCT
fcis-10203	18	14	has	have	AUX
fcis-10203	18	15	been	be	AUX
fcis-10203	18	16	proved	prove	VERB
fcis-10203	18	17	for	for	ADP
fcis-10203	18	18	machine	machine	NOUN
fcis-10203	18	19	translation	translation	NOUN
fcis-10203	18	20	problem	problem	NOUN
fcis-10203	18	21	[	[	X
fcis-10203	18	22	2	2	NUM
fcis-10203	18	23	]	]	PUNCT
fcis-10203	18	24	and	and	CCONJ
fcis-10203	18	25	then	then	ADV
fcis-10203	18	26	widely	widely	ADV
fcis-10203	18	27	used	use	VERB
fcis-10203	18	28	in	in	ADP
fcis-10203	18	29	various	various	ADJ
fcis-10203	18	30	nlp	nlp	NOUN
fcis-10203	18	31	problem	problem	NOUN
fcis-10203	18	32	(	(	PUNCT
fcis-10203	18	33	radford	radford	PROPN
fcis-10203	18	34	et	et	PROPN
fcis-10203	18	35	al	al	PROPN
fcis-10203	18	36	.	.	PROPN
fcis-10203	18	37	,	,	PUNCT
fcis-10203	18	38	2018	2018	NUM
fcis-10203	18	39	;	;	PUNCT
fcis-10203	18	40	devlin	devlin	PROPN
fcis-10203	18	41	et	et	PROPN
fcis-10203	18	42	al	al	PROPN
fcis-10203	18	43	.	.	PROPN
fcis-10203	18	44	,	,	PUNCT
fcis-10203	18	45	2018	2018	NUM
fcis-10203	18	46	;	;	PUNCT
fcis-10203	18	47	mccann	mccann	PROPN
fcis-10203	18	48	et	et	PROPN
fcis-10203	18	49	al	al	PROPN
fcis-10203	18	50	.	.	PROPN
fcis-10203	18	51	,	,	PUNCT
fcis-10203	18	52	2018	2018	NUM
fcis-10203	18	53	;	;	PUNCT
fcis-10203	18	54	yu	yu	PROPN
fcis-10203	18	55	et	et	PROPN
fcis-10203	18	56	al	al	PROPN
fcis-10203	18	57	.	.	PROPN
fcis-10203	18	58	,	,	PUNCT
fcis-10203	18	59	2018	2018	NUM
fcis-10203	18	60	)	)	PUNCT
fcis-10203	19	1	[	[	X
fcis-10203	19	2	3	3	NUM
fcis-10203	19	3	-	-	SYM
fcis-10203	19	4	6	6	NUM
fcis-10203	19	5	]	]	PUNCT
fcis-10203	19	6	.	.	PUNCT
fcis-10203	20	1	in	in	ADP
fcis-10203	20	2	2018	2018	NUM
fcis-10203	20	3	,	,	PUNCT
fcis-10203	20	4	bert	bert	PROPN
fcis-10203	20	5	was	be	AUX
fcis-10203	20	6	proposed	propose	VERB
fcis-10203	20	7	by	by	ADP
fcis-10203	20	8	google	google	PROPN
fcis-10203	20	9	.	.	PUNCT
fcis-10203	21	1	its	its	PRON
fcis-10203	21	2	“	"	PUNCT
fcis-10203	21	3	bidirectional	bidirectional	ADJ
fcis-10203	21	4	encoder	encoder	NOUN
fcis-10203	21	5	representation	representation	NOUN
fcis-10203	21	6	from	from	ADP
fcis-10203	21	7	transformers	transformer	NOUN
fcis-10203	21	8	”	"	PUNCT
fcis-10203	21	9	was	be	AUX
fcis-10203	21	10	awarded	award	VERB
fcis-10203	21	11	the	the	DET
fcis-10203	21	12	best	good	ADJ
fcis-10203	21	13	long	long	ADJ
fcis-10203	21	14	paper	paper	NOUN
fcis-10203	21	15	award	award	NOUN
fcis-10203	21	16	at	at	ADP
fcis-10203	21	17	the	the	DET
fcis-10203	21	18	2019	2019	NUM
fcis-10203	21	19	north	north	NOUN
fcis-10203	21	20	american	american	ADJ
fcis-10203	21	21	branch	branch	NOUN
fcis-10203	21	22	of	of	ADP
fcis-10203	21	23	the	the	DET
fcis-10203	21	24	association	association	NOUN
fcis-10203	21	25	for	for	ADP
fcis-10203	21	26	computational	computational	ADJ
fcis-10203	21	27	linguistics	linguistic	NOUN
fcis-10203	21	28	(	(	PUNCT
fcis-10203	21	29	naacl	naacl	PROPN
fcis-10203	21	30	)	)	PUNCT
fcis-10203	21	31	,	,	PUNCT
fcis-10203	21	32	and	and	CCONJ
fcis-10203	21	33	its	its	PRON
fcis-10203	21	34	performance	performance	NOUN
fcis-10203	21	35	on	on	ADP
fcis-10203	21	36	11	11	NUM
fcis-10203	21	37	nlp	nlp	NOUN
fcis-10203	21	38	tasks	task	NOUN
fcis-10203	21	39	has	have	AUX
fcis-10203	21	40	set	set	VERB
fcis-10203	21	41	a	a	DET
fcis-10203	21	42	new	new	ADJ
fcis-10203	21	43	record	record	NOUN
fcis-10203	21	44	[	[	X
fcis-10203	21	45	7	7	NUM
fcis-10203	21	46	]	]	PUNCT
fcis-10203	21	47	.	.	PUNCT
fcis-10203	22	1	in	in	ADP
fcis-10203	22	2	the	the	DET
fcis-10203	22	3	same	same	ADJ
fcis-10203	22	4	year	year	NOUN
fcis-10203	22	5	,	,	PUNCT
fcis-10203	22	6	the	the	DET
fcis-10203	22	7	gpt	gpt	NOUN
fcis-10203	22	8	model	model	NOUN
fcis-10203	22	9	proposed	propose	VERB
fcis-10203	22	10	by	by	ADP
fcis-10203	22	11	openai	openai	NOUN
fcis-10203	22	12	can	can	AUX
fcis-10203	22	13	be	be	AUX
fcis-10203	22	14	migrated	migrate	VERB
fcis-10203	22	15	to	to	ADP
fcis-10203	22	16	nlp	nlp	NOUN
fcis-10203	22	17	[	[	X
fcis-10203	22	18	8	8	NUM
fcis-10203	22	19	]	]	PUNCT
fcis-10203	22	20	.	.	PUNCT
fcis-10203	23	1	in	in	ADP
fcis-10203	23	2	2019	2019	NUM
fcis-10203	23	3	,	,	PUNCT
fcis-10203	23	4	bart	bart	PROPN
fcis-10203	23	5	method	method	PROPN
fcis-10203	23	6	combined	combine	VERB
fcis-10203	23	7	bert	bert	PROPN
fcis-10203	23	8	and	and	CCONJ
fcis-10203	23	9	gpt	gpt	NOUN
fcis-10203	23	10	model	model	NOUN
fcis-10203	23	11	[	[	X
fcis-10203	23	12	10	10	NUM
fcis-10203	23	13	]	]	PUNCT
fcis-10203	23	14	.	.	PUNCT
fcis-10203	24	1	its	its	PRON
fcis-10203	24	2	“	"	PUNCT
fcis-10203	24	3	bidirectional	bidirectional	ADJ
fcis-10203	24	4	and	and	CCONJ
fcis-10203	24	5	auto	auto	NOUN
fcis-10203	24	6	-	-	PUNCT
fcis-10203	24	7	regressive	regressive	ADJ
fcis-10203	24	8	transformers	transformer	NOUN
fcis-10203	24	9	”	"	PUNCT
fcis-10203	24	10	built	build	VERB
fcis-10203	24	11	a	a	DET
fcis-10203	24	12	pre	pre	NOUN
fcis-10203	24	13	training	training	NOUN
fcis-10203	24	14	language	language	NOUN
fcis-10203	24	15	model	model	NOUN
fcis-10203	24	16	by	by	ADP
fcis-10203	24	17	transformer	transformer	NOUN
fcis-10203	24	18	model	model	NOUN
fcis-10203	24	19	with	with	ADP
fcis-10203	24	20	encoder	encoder	NOUN
fcis-10203	24	21	-	-	PUNCT
fcis-10203	24	22	decoder	decoder	NOUN
fcis-10203	24	23	structure	structure	NOUN
fcis-10203	24	24	[	[	X
fcis-10203	24	25	9	9	NUM
fcis-10203	24	26	]	]	PUNCT
fcis-10203	24	27	.	.	PUNCT
fcis-10203	25	1	in	in	ADP
fcis-10203	25	2	the	the	DET
fcis-10203	25	3	same	same	ADJ
fcis-10203	25	4	year	year	NOUN
fcis-10203	25	5	,	,	PUNCT
fcis-10203	25	6	with	with	ADP
fcis-10203	25	7	the	the	DET
fcis-10203	25	8	introduction	introduction	NOUN
fcis-10203	25	9	of	of	ADP
fcis-10203	25	10	a	a	DET
fcis-10203	25	11	large	large	ADJ
fcis-10203	25	12	-	-	PUNCT
fcis-10203	25	13	scale	scale	NOUN
fcis-10203	25	14	pre	pre	NOUN
fcis-10203	25	15	training	training	NOUN
fcis-10203	25	16	model	model	NOUN
fcis-10203	25	17	,	,	PUNCT
fcis-10203	25	18	almost	almost	ADV
fcis-10203	25	19	all	all	PRON
fcis-10203	25	20	nlp	nlp	ADJ
fcis-10203	25	21	tasks	task	NOUN
fcis-10203	25	22	became	become	VERB
fcis-10203	25	23	"	"	PUNCT
fcis-10203	25	24	pre	pre	ADJ
fcis-10203	25	25	-	-	ADJ
fcis-10203	25	26	train	train	NOUN
fcis-10203	25	27	to	to	ADP
fcis-10203	25	28	fine	fine	ADJ
fcis-10203	25	29	-	-	PUNCT
fcis-10203	25	30	tune	tune	NOUN
fcis-10203	25	31	"	"	PUNCT
fcis-10203	25	32	mode	mode	NOUN
fcis-10203	25	33	.	.	PUNCT
fcis-10203	26	1	instead	instead	ADV
fcis-10203	26	2	of	of	ADP
fcis-10203	26	3	modifying	modify	VERB
fcis-10203	26	4	the	the	DET
fcis-10203	26	5	pre	pre	NOUN
fcis-10203	26	6	training	training	NOUN
fcis-10203	26	7	model	model	NOUN
fcis-10203	26	8	itself	itself	PRON
fcis-10203	26	9	,	,	PUNCT
fcis-10203	26	10	people	people	NOUN
fcis-10203	26	11	generally	generally	ADV
fcis-10203	26	12	introduced	introduce	VERB
fcis-10203	26	13	few	few	ADJ
fcis-10203	26	14	additional	additional	ADJ
fcis-10203	26	15	parameters	parameter	NOUN
fcis-10203	26	16	(	(	PUNCT
fcis-10203	26	17	network	network	NOUN
fcis-10203	26	18	layer	layer	NOUN
fcis-10203	26	19	)	)	PUNCT
fcis-10203	26	20	to	to	PART
fcis-10203	26	21	complete	complete	VERB
fcis-10203	26	22	downstream	downstream	ADJ
fcis-10203	26	23	tasks	task	NOUN
fcis-10203	26	24	by	by	ADP
fcis-10203	26	25	setting	set	VERB
fcis-10203	26	26	various	various	ADJ
fcis-10203	26	27	objective	objective	ADJ
fcis-10203	26	28	functions	function	NOUN
fcis-10203	26	29	.	.	PUNCT
fcis-10203	27	1	at	at	ADP
fcis-10203	27	2	this	this	DET
fcis-10203	27	3	point	point	NOUN
fcis-10203	27	4	,	,	PUNCT
fcis-10203	27	5	the	the	DET
fcis-10203	27	6	focus	focus	NOUN
fcis-10203	27	7	of	of	ADP
fcis-10203	27	8	work	work	NOUN
fcis-10203	27	9	has	have	AUX
fcis-10203	27	10	shifted	shift	VERB
fcis-10203	27	11	to	to	ADP
fcis-10203	27	12	objective	objective	ADJ
fcis-10203	27	13	function	function	NOUN
fcis-10203	27	14	engineering	engineering	NOUN
fcis-10203	27	15	[	[	X
fcis-10203	27	16	10	10	NUM
fcis-10203	27	17	]	]	PUNCT
fcis-10203	27	18	.	.	PUNCT
fcis-10203	28	1	in	in	ADP
fcis-10203	28	2	2020	2020	NUM
fcis-10203	28	3	,	,	PUNCT
fcis-10203	28	4	google	google	PROPN
fcis-10203	28	5	released	release	VERB
fcis-10203	28	6	the	the	DET
fcis-10203	28	7	t5	t5	PROPN
fcis-10203	28	8	model	model	NOUN
fcis-10203	28	9	.	.	PUNCT
fcis-10203	29	1	its	its	PRON
fcis-10203	29	2	most	most	ADV
fcis-10203	29	3	important	important	ADJ
fcis-10203	29	4	role	role	NOUN
fcis-10203	29	5	is	be	AUX
fcis-10203	29	6	to	to	PART
fcis-10203	29	7	provide	provide	VERB
fcis-10203	29	8	a	a	DET
fcis-10203	29	9	common	common	ADJ
fcis-10203	29	10	framework	framework	NOUN
fcis-10203	29	11	for	for	ADP
fcis-10203	29	12	the	the	DET
fcis-10203	29	13	entire	entire	ADJ
fcis-10203	29	14	nlp	nlp	NOUN
fcis-10203	29	15	pre	pre	ADJ
fcis-10203	29	16	training	training	NOUN
fcis-10203	29	17	model	model	NOUN
fcis-10203	29	18	field	field	NOUN
fcis-10203	29	19	,	,	PUNCT
fcis-10203	29	20	transforming	transform	VERB
fcis-10203	29	21	all	all	DET
fcis-10203	29	22	tasks	task	NOUN
fcis-10203	29	23	into	into	ADP
fcis-10203	29	24	one	one	NUM
fcis-10203	29	25	form	form	NOUN
fcis-10203	29	26	.	.	PUNCT
fcis-10203	30	1	after	after	ADP
fcis-10203	30	2	that	that	PRON
fcis-10203	30	3	,	,	PUNCT
fcis-10203	30	4	the	the	DET
fcis-10203	30	5	main	main	ADJ
fcis-10203	30	6	task	task	NOUN
fcis-10203	30	7	became	become	VERB
fcis-10203	30	8	how	how	SCONJ
fcis-10203	30	9	to	to	PART
fcis-10203	30	10	convert	convert	VERB
fcis-10203	30	11	tasks	task	NOUN
fcis-10203	30	12	into	into	ADP
fcis-10203	30	13	appropriate	appropriate	ADJ
fcis-10203	30	14	text	text	NOUN
fcis-10203	30	15	-	-	PUNCT
fcis-10203	30	16	input	input	NOUN
fcis-10203	30	17	and	and	CCONJ
fcis-10203	30	18	text	text	NOUN
fcis-10203	30	19	-	-	PUNCT
fcis-10203	30	20	output	output	NOUN
fcis-10203	30	21	[	[	X
fcis-10203	30	22	11	11	NUM
fcis-10203	30	23	]	]	PUNCT
fcis-10203	30	24	.	.	PUNCT
fcis-10203	31	1	however	however	ADV
fcis-10203	31	2	,	,	PUNCT
fcis-10203	31	3	as	as	ADP
fcis-10203	31	4	pre	pre	ADJ
fcis-10203	31	5	-	-	ADJ
fcis-10203	31	6	trained	train	VERB
fcis-10203	31	7	language	language	NOUN
fcis-10203	31	8	models	model	NOUN
fcis-10203	31	9	(	(	PUNCT
fcis-10203	31	10	plms	plm	NOUN
fcis-10203	31	11	)	)	PUNCT
fcis-10203	31	12	become	become	VERB
fcis-10203	31	13	larger	large	ADJ
fcis-10203	31	14	and	and	CCONJ
fcis-10203	31	15	larger	large	ADJ
fcis-10203	31	16	,	,	PUNCT
fcis-10203	31	17	the	the	DET
fcis-10203	31	18	requirements	requirement	NOUN
fcis-10203	31	19	of	of	ADP
fcis-10203	31	20	hardware	hardware	NOUN
fcis-10203	31	21	,	,	PUNCT
fcis-10203	31	22	data	datum	NOUN
fcis-10203	31	23	,	,	PUNCT
fcis-10203	31	24	and	and	CCONJ
fcis-10203	31	25	actual	actual	ADJ
fcis-10203	31	26	costs	cost	NOUN
fcis-10203	31	27	are	be	AUX
fcis-10203	31	28	also	also	ADV
fcis-10203	31	29	increasing	increase	VERB
fcis-10203	31	30	.	.	PUNCT
fcis-10203	32	1	what	what	PRON
fcis-10203	32	2	’s	’	VERB
fcis-10203	32	3	more	more	ADJ
fcis-10203	32	4	,	,	PUNCT
fcis-10203	32	5	the	the	DET
fcis-10203	32	6	design	design	NOUN
fcis-10203	32	7	of	of	ADP
fcis-10203	32	8	the	the	DET
fcis-10203	32	9	pre	pre	NOUN
fcis-10203	32	10	-	-	NOUN
fcis-10203	32	11	training	training	ADJ
fcis-10203	32	12	and	and	CCONJ
fcis-10203	32	13	fine	fine	ADV
fcis-10203	32	14	-	-	PUNCT
fcis-10203	32	15	tuning	tune	VERB
fcis-10203	32	16	stages	stage	NOUN
fcis-10203	32	17	become	become	VERB
fcis-10203	32	18	complex	complex	ADJ
fcis-10203	32	19	as	as	ADP
fcis-10203	32	20	the	the	DET
fcis-10203	32	21	result	result	NOUN
fcis-10203	32	22	of	of	ADP
fcis-10203	32	23	the	the	DET
fcis-10203	32	24	large	large	ADJ
fcis-10203	32	25	and	and	CCONJ
fcis-10203	32	26	diverse	diverse	ADJ
fcis-10203	32	27	downstream	downstream	ADJ
fcis-10203	32	28	tasks	task	NOUN
fcis-10203	32	29	.	.	PUNCT
fcis-10203	33	1	in	in	ADP
fcis-10203	33	2	order	order	NOUN
fcis-10203	33	3	to	to	PART
fcis-10203	33	4	explore	explore	VERB
fcis-10203	33	5	smaller	small	ADJ
fcis-10203	33	6	,	,	PUNCT
fcis-10203	33	7	more	more	ADV
fcis-10203	33	8	lightweight	lightweight	ADJ
fcis-10203	33	9	,	,	PUNCT
fcis-10203	33	10	and	and	CCONJ
fcis-10203	33	11	more	more	ADV
fcis-10203	33	12	universal	universal	ADJ
fcis-10203	33	13	and	and	CCONJ
fcis-10203	33	14	efficient	efficient	ADJ
fcis-10203	33	15	methods	method	NOUN
fcis-10203	33	16	,	,	PUNCT
fcis-10203	33	17	researchers	researcher	NOUN
fcis-10203	33	18	attempt	attempt	VERB
fcis-10203	33	19	to	to	PART
fcis-10203	33	20	use	use	VERB
fcis-10203	33	21	“	"	PUNCT
fcis-10203	33	22	prompt	prompt	ADJ
fcis-10203	33	23	”	"	PUNCT
fcis-10203	33	24	method	method	NOUN
fcis-10203	33	25	.	.	PUNCT
fcis-10203	34	1	in	in	ADP
fcis-10203	34	2	2021	2021	NUM
fcis-10203	34	3	,	,	PUNCT
fcis-10203	34	4	"	"	PUNCT
fcis-10203	34	5	pre	pre	X
fcis-10203	34	6	train	train	NOUN
fcis-10203	34	7	,	,	PUNCT
fcis-10203	34	8	prompt	prompt	ADJ
fcis-10203	34	9	,	,	PUNCT
fcis-10203	34	10	and	and	CCONJ
fcis-10203	34	11	predict	predict	VERB
fcis-10203	34	12	"	"	PUNCT
fcis-10203	34	13	was	be	AUX
fcis-10203	34	14	introduced	introduce	VERB
fcis-10203	34	15	and	and	CCONJ
fcis-10203	34	16	the	the	DET
fcis-10203	34	17	original	original	ADJ
fcis-10203	34	18	"	"	PUNCT
fcis-10203	34	19	pre	pre	NOUN
fcis-10203	34	20	-	-	NOUN
fcis-10203	34	21	train	train	NOUN
fcis-10203	34	22	to	to	ADP
fcis-10203	34	23	fine	fine	ADJ
fcis-10203	34	24	-	-	PUNCT
fcis-10203	34	25	tune	tune	NOUN
fcis-10203	34	26	"	"	PUNCT
fcis-10203	34	27	mode	mode	NOUN
fcis-10203	34	28	has	have	AUX
fcis-10203	34	29	gradually	gradually	ADV
fcis-10203	34	30	been	be	AUX
fcis-10203	34	31	replaced	replace	VERB
fcis-10203	34	32	by	by	ADP
fcis-10203	34	33	this	this	DET
fcis-10203	34	34	mode	mode	NOUN
fcis-10203	34	35	.	.	PUNCT
fcis-10203	35	1	people	people	NOUN
fcis-10203	35	2	no	no	ADV
fcis-10203	35	3	longer	long	ADV
fcis-10203	35	4	use	use	VERB
fcis-10203	35	5	customized	customize	VERB
fcis-10203	35	6	objective	objective	ADJ
fcis-10203	35	7	function	function	NOUN
fcis-10203	35	8	engineering	engineering	NOUN
fcis-10203	35	9	to	to	PART
fcis-10203	35	10	adapt	adapt	VERB
fcis-10203	35	11	pre	pre	NOUN
fcis-10203	35	12	training	training	NOUN
fcis-10203	35	13	models	model	NOUN
fcis-10203	35	14	to	to	ADP
fcis-10203	35	15	downstream	downstream	ADJ
fcis-10203	35	16	tasks	task	NOUN
fcis-10203	35	17	.	.	PUNCT
fcis-10203	36	1	instead	instead	ADV
fcis-10203	36	2	,	,	PUNCT
fcis-10203	36	3	various	various	ADJ
fcis-10203	36	4	downstream	downstream	ADJ
fcis-10203	36	5	tasks	task	NOUN
fcis-10203	36	6	are	be	AUX
fcis-10203	36	7	redefined	redefine	VERB
fcis-10203	36	8	under	under	ADP
fcis-10203	36	9	a	a	DET
fcis-10203	36	10	short	short	ADJ
fcis-10203	36	11	text	text	NOUN
fcis-10203	36	12	prompt	prompt	ADJ
fcis-10203	36	13	to	to	PART
fcis-10203	36	14	resemble	resemble	VERB
fcis-10203	36	15	as	as	ADV
fcis-10203	36	16	much	much	ADV
fcis-10203	36	17	as	as	ADP
fcis-10203	36	18	possible	possible	ADJ
fcis-10203	36	19	the	the	DET
fcis-10203	36	20	problem	problem	NOUN
fcis-10203	36	21	forms	form	VERB
fcis-10203	36	22	that	that	SCONJ
fcis-10203	36	23	plms	plm	NOUN
fcis-10203	36	24	solves	solve	VERB
fcis-10203	36	25	during	during	ADP
fcis-10203	36	26	training	training	NOUN
fcis-10203	36	27	[	[	X
fcis-10203	36	28	12	12	NUM
fcis-10203	36	29	]	]	PUNCT
fcis-10203	36	30	.	.	PUNCT
fcis-10203	37	1	1.2	1.2	NUM
fcis-10203	37	2	.	.	PUNCT
fcis-10203	37	3	related	relate	VERB
fcis-10203	37	4	works	work	NOUN
fcis-10203	37	5	qa	qa	PROPN
fcis-10203	37	6	tasks	task	NOUN
fcis-10203	37	7	is	be	AUX
fcis-10203	37	8	a	a	DET
fcis-10203	37	9	kind	kind	NOUN
fcis-10203	37	10	of	of	ADP
fcis-10203	37	11	downstream	downstream	ADJ
fcis-10203	37	12	tasks	task	NOUN
fcis-10203	37	13	in	in	ADP
fcis-10203	37	14	nlp	nlp	NOUN
fcis-10203	37	15	.	.	PUNCT
fcis-10203	38	1	although	although	SCONJ
fcis-10203	38	2	the	the	DET
fcis-10203	38	3	qa	qa	PROPN
fcis-10203	38	4	task	task	NOUN
fcis-10203	38	5	forms	form	NOUN
fcis-10203	38	6	are	be	AUX
fcis-10203	38	7	different	different	ADJ
fcis-10203	38	8	,	,	PUNCT
fcis-10203	38	9	the	the	DET
fcis-10203	38	10	semantic	semantic	ADJ
fcis-10203	38	11	understanding	understanding	NOUN
fcis-10203	38	12	and	and	CCONJ
fcis-10203	38	13	reasoning	reasoning	NOUN
fcis-10203	38	14	capabilities	capability	NOUN
fcis-10203	38	15	required	require	VERB
fcis-10203	38	16	by	by	ADP
fcis-10203	38	17	models	model	NOUN
fcis-10203	38	18	are	be	AUX
fcis-10203	38	19	common	common	ADJ
fcis-10203	38	20	,	,	PUNCT
fcis-10203	38	21	and	and	CCONJ
fcis-10203	38	22	may	may	AUX
fcis-10203	38	23	not	not	PART
fcis-10203	38	24	require	require	VERB
fcis-10203	38	25	format	format	NOUN
fcis-10203	38	26	specific	specific	ADJ
fcis-10203	38	27	models	model	NOUN
fcis-10203	38	28	.	.	PUNCT
fcis-10203	39	1	based	base	VERB
fcis-10203	39	2	on	on	ADP
fcis-10203	39	3	this	this	DET
fcis-10203	39	4	concept	concept	NOUN
fcis-10203	39	5	,	,	PUNCT
fcis-10203	39	6	allen	allen	PROPN
fcis-10203	39	7	institute	institute	PROPN
fcis-10203	39	8	and	and	CCONJ
fcis-10203	39	9	the	the	DET
fcis-10203	39	10	university	university	PROPN
fcis-10203	39	11	of	of	ADP
fcis-10203	39	12	washington	washington	PROPN
fcis-10203	39	13	proposed	propose	VERB
fcis-10203	39	14	the	the	DET
fcis-10203	39	15	first	first	ADJ
fcis-10203	39	16	pre	pre	ADJ
fcis-10203	39	17	training	training	NOUN
fcis-10203	39	18	question	question	NOUN
fcis-10203	39	19	answering	answer	VERB
fcis-10203	39	20	model	model	NOUN
fcis-10203	39	21	,	,	PUNCT
fcis-10203	39	22	unifiedqa	unifiedqa	X
fcis-10203	39	23	,	,	PUNCT
fcis-10203	39	24	on	on	ADP
fcis-10203	39	25	emnlp	emnlp	ADV
fcis-10203	39	26	in	in	ADP
fcis-10203	39	27	november	november	PROPN
fcis-10203	39	28	2020	2020	NUM
fcis-10203	39	29	,	,	PUNCT
fcis-10203	39	30	which	which	PRON
fcis-10203	39	31	can	can	AUX
fcis-10203	39	32	handle	handle	VERB
fcis-10203	39	33	multiple	multiple	ADJ
fcis-10203	39	34	forms	form	NOUN
fcis-10203	39	35	of	of	ADP
fcis-10203	39	36	questions	question	NOUN
fcis-10203	39	37	and	and	CCONJ
fcis-10203	39	38	answers	answer	NOUN
fcis-10203	39	39	,	,	PUNCT
fcis-10203	39	40	becoming	become	VERB
fcis-10203	39	41	a	a	DET
fcis-10203	39	42	new	new	ADJ
fcis-10203	39	43	sota	sota	NOUN
fcis-10203	39	44	for	for	ADP
fcis-10203	39	45	multiple	multiple	ADJ
fcis-10203	39	46	question	question	NOUN
fcis-10203	39	47	answering	answering	NOUN
fcis-10203	39	48	tasks	task	NOUN
fcis-10203	39	49	.	.	PUNCT
fcis-10203	40	1	all	all	DET
fcis-10203	40	2	nlp	nlp	NOUN
fcis-10203	40	3	tasks	task	NOUN
fcis-10203	40	4	can	can	AUX
fcis-10203	40	5	be	be	AUX
fcis-10203	40	6	converted	convert	VERB
fcis-10203	40	7	to	to	ADP
fcis-10203	40	8	seq2seq	seq2seq	NOUN
fcis-10203	40	9	tasks	task	NOUN
fcis-10203	40	10	.	.	PUNCT
fcis-10203	41	1	based	base	VERB
fcis-10203	41	2	on	on	ADP
fcis-10203	41	3	the	the	DET
fcis-10203	41	4	same	same	ADJ
fcis-10203	41	5	idea	idea	NOUN
fcis-10203	41	6	,	,	PUNCT
fcis-10203	41	7	unifiedqa	unifiedqa	PROPN
fcis-10203	41	8	is	be	AUX
fcis-10203	41	9	a	a	DET
fcis-10203	41	10	text	text	NOUN
fcis-10203	41	11	-	-	PUNCT
fcis-10203	41	12	to	to	ADP
fcis-10203	41	13	-	-	PUNCT
fcis-10203	41	14	text	text	VERB
fcis-10203	41	15	pre	pre	ADJ
fcis-10203	41	16	training	training	NOUN
fcis-10203	41	17	question	question	NOUN
fcis-10203	41	18	answering	answering	NOUN
fcis-10203	41	19	model	model	NOUN
fcis-10203	41	20	.	.	PUNCT
fcis-10203	42	1	the	the	DET
fcis-10203	42	2	encoder	encoder	NOUN
fcis-10203	42	3	receives	receive	VERB
fcis-10203	42	4	questions	question	NOUN
fcis-10203	42	5	spliced	splice	VERB
fcis-10203	42	6	with	with	ADP
fcis-10203	42	7	"	"	PUNCT
fcis-10203	42	8	\n	\n	X
fcis-10203	42	9	"	"	PUNCT
fcis-10203	42	10	,	,	PUNCT
fcis-10203	42	11	and	and	CCONJ
fcis-10203	42	12	the	the	DET
fcis-10203	42	13	decoder	decoder	NOUN
fcis-10203	42	14	generates	generate	VERB
fcis-10203	42	15	answers	answer	NOUN
fcis-10203	42	16	[	[	X
fcis-10203	42	17	13	13	NUM
fcis-10203	42	18	]	]	PUNCT
fcis-10203	42	19	.	.	PUNCT
fcis-10203	43	1	in	in	ADP
fcis-10203	43	2	2022	2022	NUM
fcis-10203	43	3	,	,	PUNCT
fcis-10203	43	4	the	the	DET
fcis-10203	43	5	original	original	ADJ
fcis-10203	43	6	team	team	NOUN
fcis-10203	43	7	only	only	ADV
fcis-10203	43	8	added	add	VERB
fcis-10203	43	9	more	more	ADJ
fcis-10203	43	10	pre	pre	ADJ
fcis-10203	43	11	training	training	NOUN
fcis-10203	43	12	datasets	dataset	NOUN
fcis-10203	43	13	to	to	ADP
fcis-10203	43	14	the	the	DET
fcis-10203	43	15	original	original	ADJ
fcis-10203	43	16	unifiedqa	unifiedqa	NOUN
fcis-10203	43	17	for	for	ADP
fcis-10203	43	18	pre	pre	NOUN
fcis-10203	43	19	training	training	NOUN
fcis-10203	43	20	,	,	PUNCT
fcis-10203	43	21	which	which	PRON
fcis-10203	43	22	further	far	ADV
fcis-10203	43	23	improved	improve	VERB
fcis-10203	43	24	the	the	DET
fcis-10203	43	25	performance	performance	NOUN
fcis-10203	43	26	of	of	ADP
fcis-10203	43	27	the	the	DET
fcis-10203	43	28	model	model	NOUN
fcis-10203	43	29	on	on	ADP
fcis-10203	43	30	both	both	CCONJ
fcis-10203	43	31	the	the	DET
fcis-10203	43	32	"	"	PUNCT
fcis-10203	43	33	seen	see	VERB
fcis-10203	43	34	"	"	PUNCT
fcis-10203	43	35	dataset	dataset	NOUN
fcis-10203	43	36	and	and	CCONJ
fcis-10203	43	37	the	the	DET
fcis-10203	43	38	"	"	PUNCT
fcis-10203	43	39	unseen	unseen	ADJ
fcis-10203	43	40	"	"	PUNCT
fcis-10203	43	41	dataset	dataset	VERB
fcis-10203	43	42	to	to	ADP
fcis-10203	43	43	unifiedqa	unifiedqa	VERB
fcis-10203	43	44	-	-	PUNCT
fcis-10203	43	45	v2[14	v2[14	NOUN
fcis-10203	43	46	]	]	PUNCT
fcis-10203	43	47	.	.	PUNCT
fcis-10203	44	1	at	at	ADP
fcis-10203	44	2	the	the	DET
fcis-10203	44	3	same	same	ADJ
fcis-10203	44	4	time	time	NOUN
fcis-10203	44	5	,	,	PUNCT
fcis-10203	44	6	gpt-2	gpt-2	PUNCT
fcis-10203	44	7	had	have	VERB
fcis-10203	44	8	1.5	1.5	NUM
fcis-10203	44	9	billion	billion	NUM
fcis-10203	44	10	parameters	parameter	NOUN
fcis-10203	44	11	in	in	ADP
fcis-10203	44	12	64	64	NUM
fcis-10203	44	13	2019[15	2019[15	NUM
fcis-10203	44	14	]	]	PUNCT
fcis-10203	44	15	.	.	PUNCT
fcis-10203	45	1	in	in	ADP
fcis-10203	45	2	2020	2020	NUM
fcis-10203	45	3	,	,	PUNCT
fcis-10203	45	4	gpt-3	gpt-3	X
fcis-10203	45	5	already	already	ADV
fcis-10203	45	6	had	have	VERB
fcis-10203	45	7	an	an	DET
fcis-10203	45	8	astonishing	astonishing	ADJ
fcis-10203	45	9	175	175	NUM
fcis-10203	45	10	billion	billion	NUM
fcis-10203	45	11	parameters	parameter	NOUN
fcis-10203	45	12	[	[	X
fcis-10203	45	13	16	16	NUM
fcis-10203	45	14	]	]	PUNCT
fcis-10203	45	15	.	.	PUNCT
fcis-10203	46	1	in	in	ADP
fcis-10203	46	2	2022	2022	NUM
fcis-10203	46	3	,	,	PUNCT
fcis-10203	46	4	instrumentgpt	instrumentgpt	NOUN
fcis-10203	46	5	and	and	CCONJ
fcis-10203	46	6	chatgpt	chatgpt	NOUN
fcis-10203	46	7	[	[	X
fcis-10203	46	8	17	17	NUM
fcis-10203	46	9	]	]	PUNCT
fcis-10203	46	10	.	.	PUNCT
fcis-10203	47	1	on	on	ADP
fcis-10203	47	2	march	march	PROPN
fcis-10203	47	3	14	14	NUM
fcis-10203	47	4	,	,	PUNCT
fcis-10203	47	5	2023	2023	NUM
fcis-10203	47	6	,	,	PUNCT
fcis-10203	47	7	gpt-4	gpt-4	PRON
fcis-10203	47	8	was	be	AUX
fcis-10203	47	9	released	release	VERB
fcis-10203	47	10	[	[	PUNCT
fcis-10203	47	11	18	18	NUM
fcis-10203	47	12	]	]	PUNCT
fcis-10203	47	13	.	.	PUNCT
fcis-10203	48	1	this	this	DET
fcis-10203	48	2	paper	paper	NOUN
fcis-10203	48	3	focuses	focus	VERB
fcis-10203	48	4	on	on	ADP
fcis-10203	48	5	constructing	construct	VERB
fcis-10203	48	6	a	a	DET
fcis-10203	48	7	question	question	NOUN
fcis-10203	48	8	answering	answer	VERB
fcis-10203	48	9	system	system	NOUN
fcis-10203	48	10	based	base	VERB
fcis-10203	48	11	on	on	ADP
fcis-10203	48	12	spqa	spqa	NOUN
fcis-10203	48	13	for	for	ADP
fcis-10203	48	14	situation	situation	NOUN
fcis-10203	48	15	puzzles	puzzle	NOUN
fcis-10203	48	16	.	.	PUNCT
fcis-10203	49	1	regarding	regard	VERB
fcis-10203	49	2	unfiedqa	unfiedqa	ADJ
fcis-10203	49	3	-	-	PUNCT
fcis-10203	49	4	v2	v2	NOUN
fcis-10203	49	5	model	model	NOUN
fcis-10203	49	6	as	as	ADP
fcis-10203	49	7	original	original	ADJ
fcis-10203	49	8	model	model	NOUN
fcis-10203	49	9	and	and	CCONJ
fcis-10203	49	10	adding	add	VERB
fcis-10203	49	11	situation	situation	NOUN
fcis-10203	49	12	puzzles	puzzle	NOUN
fcis-10203	49	13	training	training	NOUN
fcis-10203	49	14	set	set	NOUN
fcis-10203	49	15	,	,	PUNCT
fcis-10203	49	16	i	i	PRON
fcis-10203	49	17	constructed	construct	VERB
fcis-10203	49	18	a	a	DET
fcis-10203	49	19	situation	situation	NOUN
fcis-10203	49	20	-	-	PUNCT
fcis-10203	49	21	puzzle	puzzle	NOUN
fcis-10203	49	22	qa	qa	PROPN
fcis-10203	49	23	model	model	NOUN
fcis-10203	49	24	(	(	PUNCT
fcis-10203	49	25	spqa	spqa	PROPN
fcis-10203	49	26	)	)	PUNCT
fcis-10203	49	27	and	and	CCONJ
fcis-10203	49	28	then	then	ADV
fcis-10203	49	29	a	a	DET
fcis-10203	49	30	spqa	spqa	ADJ
fcis-10203	49	31	prompt	prompt	ADJ
fcis-10203	49	32	tuning	tuning	NOUN
fcis-10203	49	33	model	model	NOUN
fcis-10203	49	34	.	.	PUNCT
fcis-10203	50	1	eventually	eventually	ADV
fcis-10203	50	2	,	,	PUNCT
fcis-10203	50	3	i	i	PRON
fcis-10203	50	4	compared	compare	VERB
fcis-10203	50	5	the	the	DET
fcis-10203	50	6	appearance	appearance	NOUN
fcis-10203	50	7	of	of	ADP
fcis-10203	50	8	unifiedqa	unifiedqa	NOUN
fcis-10203	50	9	,	,	PUNCT
fcis-10203	50	10	spqa	spqa	ADJ
fcis-10203	50	11	,	,	PUNCT
fcis-10203	50	12	spqa	spqa	NOUN
fcis-10203	50	13	-	-	PUNCT
fcis-10203	50	14	prompt	prompt	NOUN
fcis-10203	50	15	and	and	CCONJ
fcis-10203	50	16	chatgpt	chatgpt	NOUN
fcis-10203	50	17	(	(	PUNCT
fcis-10203	50	18	gpt-3.5	gpt-3.5	NOUN
fcis-10203	50	19	)	)	PUNCT
fcis-10203	50	20	on	on	ADP
fcis-10203	50	21	solving	solve	VERB
fcis-10203	50	22	situationpuzzle	situationpuzzle	NOUN
fcis-10203	50	23	problem	problem	NOUN
fcis-10203	50	24	.	.	PUNCT
fcis-10203	51	1	2	2	X
fcis-10203	51	2	.	.	X
fcis-10203	51	3	methodology	methodology	NOUN
fcis-10203	51	4	in	in	ADP
fcis-10203	51	5	this	this	DET
fcis-10203	51	6	paper	paper	NOUN
fcis-10203	51	7	,	,	PUNCT
fcis-10203	51	8	i	i	PRON
fcis-10203	51	9	used	use	VERB
fcis-10203	51	10	two	two	NUM
fcis-10203	51	11	methods	method	NOUN
fcis-10203	51	12	:	:	PUNCT
fcis-10203	51	13	unifiedqa	unifiedqa	PROPN
fcis-10203	51	14	(	(	PUNCT
fcis-10203	51	15	v1	v1	NOUN
fcis-10203	51	16	and	and	CCONJ
fcis-10203	51	17	v2	v2	NOUN
fcis-10203	51	18	)	)	PUNCT
fcis-10203	51	19	multi	multi	ADJ
fcis-10203	51	20	-	-	ADJ
fcis-10203	51	21	format	format	NOUN
fcis-10203	51	22	training	training	NOUN
fcis-10203	51	23	and	and	CCONJ
fcis-10203	51	24	fine	fine	ADJ
fcis-10203	51	25	tuning	tuning	NOUN
fcis-10203	51	26	,	,	PUNCT
fcis-10203	51	27	and	and	CCONJ
fcis-10203	51	28	parameter	parameter	NOUN
fcis-10203	51	29	-	-	PUNCT
fcis-10203	51	30	efficient	efficient	ADJ
fcis-10203	51	31	prompt	prompt	ADJ
fcis-10203	51	32	tuning	tuning	NOUN
fcis-10203	51	33	aim	aim	VERB
fcis-10203	51	34	to	to	PART
fcis-10203	51	35	evaluate	evaluate	VERB
fcis-10203	51	36	the	the	DET
fcis-10203	51	37	performance	performance	NOUN
fcis-10203	51	38	of	of	ADP
fcis-10203	51	39	adding	add	VERB
fcis-10203	51	40	spq	spq	NOUN
fcis-10203	51	41	datasets	dataset	NOUN
fcis-10203	51	42	.	.	PUNCT
fcis-10203	52	1	2.1	2.1	NUM
fcis-10203	52	2	.	.	PUNCT
fcis-10203	53	1	multi	multi	ADJ
fcis-10203	53	2	-	-	ADJ
fcis-10203	53	3	format	format	ADJ
fcis-10203	53	4	training	training	NOUN
fcis-10203	53	5	in	in	ADP
fcis-10203	53	6	unifiedqa	unifiedqa	NOUN
fcis-10203	53	7	firstly	firstly	ADV
fcis-10203	53	8	,	,	PUNCT
fcis-10203	53	9	i	i	PRON
fcis-10203	53	10	want	want	VERB
fcis-10203	53	11	to	to	PART
fcis-10203	53	12	train	train	VERB
fcis-10203	53	13	a	a	DET
fcis-10203	53	14	spqa	spqa	ADJ
fcis-10203	53	15	model	model	NOUN
fcis-10203	53	16	that	that	PRON
fcis-10203	53	17	can	can	AUX
fcis-10203	53	18	operate	operate	VERB
fcis-10203	53	19	over	over	ADP
fcis-10203	53	20	formats	format	NOUN
fcis-10203	53	21	,	,	PUNCT
fcis-10203	53	22	,	,	PUNCT
fcis-10203	53	23	…	…	PUNCT
fcis-10203	53	24	,	,	PUNCT
fcis-10203	53	25	,	,	PUNCT
fcis-10203	53	26	like	like	ADP
fcis-10203	53	27	the	the	DET
fcis-10203	53	28	structure	structure	NOUN
fcis-10203	53	29	of	of	ADP
fcis-10203	53	30	unifiedqa	unifiedqa	PROPN
fcis-10203	53	31	model	model	NOUN
fcis-10203	53	32	.	.	PUNCT
fcis-10203	54	1	for	for	ADP
fcis-10203	54	2	each	each	DET
fcis-10203	54	3	format	format	NOUN
fcis-10203	54	4	,	,	PUNCT
fcis-10203	54	5	there	there	PRON
fcis-10203	54	6	is	be	VERB
fcis-10203	54	7	ℓ	ℓ	PROPN
fcis-10203	54	8	datasets	dataset	NOUN
fcis-10203	54	9	set	set	VERB
fcis-10203	54	10	:	:	PUNCT
fcis-10203	54	11	,	,	PUNCT
fcis-10203	54	12	,	,	PUNCT
fcis-10203	54	13	…	…	PUNCT
fcis-10203	54	14	,	,	PUNCT
fcis-10203	54	15	ℓ	ℓ	INTJ
fcis-10203	54	16	,	,	PUNCT
fcis-10203	54	17	where	where	SCONJ
fcis-10203	54	18	,	,	PUNCT
fcis-10203	54	19	,	,	PUNCT
fcis-10203	54	20	which	which	PRON
fcis-10203	54	21	includes	include	VERB
fcis-10203	54	22	training	training	NOUN
fcis-10203	54	23	set	set	NOUN
fcis-10203	54	24	and	and	CCONJ
fcis-10203	54	25	evaluation	evaluation	NOUN
fcis-10203	54	26	set	set	NOUN
fcis-10203	54	27	.	.	PUNCT
fcis-10203	55	1	if	if	SCONJ
fcis-10203	55	2	the	the	DET
fcis-10203	55	3	dataset	dataset	NOUN
fcis-10203	55	4	is	be	AUX
fcis-10203	55	5	considered	consider	VERB
fcis-10203	55	6	to	to	PART
fcis-10203	55	7	be	be	AUX
fcis-10203	55	8	used	use	VERB
fcis-10203	55	9	only	only	ADV
fcis-10203	55	10	for	for	ADP
fcis-10203	55	11	evaluation	evaluation	NOUN
fcis-10203	55	12	,	,	PUNCT
fcis-10203	55	13	i	i	PRON
fcis-10203	55	14	will	will	AUX
fcis-10203	55	15	ignore	ignore	VERB
fcis-10203	55	16	the	the	DET
fcis-10203	55	17	aim	aim	NOUN
fcis-10203	55	18	to	to	PART
fcis-10203	55	19	treat	treat	VERB
fcis-10203	55	20	as	as	ADP
fcis-10203	55	21	an	an	DET
fcis-10203	55	22	“	"	PUNCT
fcis-10203	55	23	unseen	unseen	ADJ
fcis-10203	55	24	”	"	PUNCT
fcis-10203	55	25	dataset	dataset	NOUN
fcis-10203	55	26	.	.	PUNCT
fcis-10203	56	1	in	in	ADP
fcis-10203	56	2	spqa	spqa	PROPN
fcis-10203	56	3	model	model	NOUN
fcis-10203	56	4	,	,	PUNCT
fcis-10203	56	5	the	the	DET
fcis-10203	56	6	“	"	PUNCT
fcis-10203	56	7	unseen	unseen	ADJ
fcis-10203	56	8	”	"	PUNCT
fcis-10203	56	9	dataset	dataset	NOUN
fcis-10203	56	10	only	only	ADV
fcis-10203	56	11	includes	include	VERB
fcis-10203	56	12	“	"	PUNCT
fcis-10203	56	13	yes	yes	INTJ
fcis-10203	56	14	/	/	SYM
fcis-10203	56	15	no	no	DET
fcis-10203	56	16	questions	question	NOUN
fcis-10203	56	17	”	"	PUNCT
fcis-10203	56	18	format	format	NOUN
fcis-10203	56	19	datasets	dataset	NOUN
fcis-10203	56	20	.	.	PUNCT
fcis-10203	57	1	in	in	ADP
fcis-10203	57	2	pre	pre	ADJ
fcis-10203	57	3	-	-	ADJ
fcis-10203	57	4	processing	processing	ADJ
fcis-10203	57	5	progress	progress	NOUN
fcis-10203	57	6	,	,	PUNCT
fcis-10203	57	7	i	i	PRON
fcis-10203	57	8	also	also	ADV
fcis-10203	57	9	transfer	transfer	VERB
fcis-10203	57	10	each	each	DET
fcis-10203	57	11	training	training	NOUN
fcis-10203	57	12	question	question	NOUN
fcis-10203	57	13	in	in	ADP
fcis-10203	57	14	format	format	NOUN
fcis-10203	57	15	into	into	ADP
fcis-10203	57	16	a	a	DET
fcis-10203	57	17	plain	plain	ADJ
fcis-10203	57	18	-	-	PUNCT
fcis-10203	57	19	text	text	NOUN
fcis-10203	57	20	input	input	NOUN
fcis-10203	57	21	representation	representation	NOUN
fcis-10203	57	22	,	,	PUNCT
fcis-10203	57	23	which	which	PRON
fcis-10203	57	24	is	be	AUX
fcis-10203	57	25	the	the	DET
fcis-10203	57	26	same	same	ADJ
fcis-10203	57	27	as	as	ADP
fcis-10203	57	28	that	that	PRON
fcis-10203	57	29	of	of	ADP
fcis-10203	57	30	unifiedqa	unifiedqa	ADJ
fcis-10203	57	31	training	training	NOUN
fcis-10203	57	32	datasets	dataset	NOUN
fcis-10203	57	33	.	.	PUNCT
fcis-10203	58	1	i	i	PRON
fcis-10203	58	2	use	use	VERB
fcis-10203	58	3	the	the	DET
fcis-10203	58	4	unifiedqa	unifiedqa	ADJ
fcis-10203	58	5	approach	approach	NOUN
fcis-10203	58	6	of	of	ADP
fcis-10203	58	7	creating	create	VERB
fcis-10203	58	8	a	a	DET
fcis-10203	58	9	mixed	mixed	ADJ
fcis-10203	58	10	training	training	NOUN
fcis-10203	58	11	pool	pool	NOUN
fcis-10203	58	12	including	include	VERB
fcis-10203	58	13	all	all	DET
fcis-10203	58	14	available	available	ADJ
fcis-10203	58	15	training	training	NOUN
fcis-10203	58	16	examples	example	NOUN
fcis-10203	58	17	:	:	PUNCT
fcis-10203	58	18	⋃	⋃	PUNCT
fcis-10203	58	19	⋃	⋃	PROPN
fcis-10203	58	20	|	|	NOUN
fcis-10203	58	21	∈ℓ	∈ℓ	NOUN
fcis-10203	58	22	(	(	PUNCT
fcis-10203	58	23	1	1	NUM
fcis-10203	58	24	)	)	PUNCT
fcis-10203	58	25	2.2	2.2	NUM
fcis-10203	58	26	.	.	PUNCT
fcis-10203	59	1	adapter	adapter	NOUN
fcis-10203	59	2	tuning	tuning	NOUN
fcis-10203	59	3	and	and	CCONJ
fcis-10203	59	4	parameter	parameter	NOUN
fcis-10203	59	5	-	-	PUNCT
fcis-10203	59	6	efficient	efficient	ADJ
fcis-10203	59	7	prompt	prompt	ADJ
fcis-10203	59	8	tuning	tune	VERB
fcis-10203	59	9	adapter	adapter	NOUN
fcis-10203	59	10	tuning	tuning	NOUN
fcis-10203	59	11	is	be	AUX
fcis-10203	59	12	related	relate	VERB
fcis-10203	59	13	to	to	ADP
fcis-10203	59	14	multi	multi	ADJ
fcis-10203	59	15	-	-	NOUN
fcis-10203	59	16	task	task	NOUN
fcis-10203	59	17	and	and	CCONJ
fcis-10203	59	18	continual	continual	ADJ
fcis-10203	59	19	learning	learning	NOUN
fcis-10203	59	20	but	but	CCONJ
fcis-10203	59	21	also	also	ADV
fcis-10203	59	22	differ	differ	VERB
fcis-10203	59	23	because	because	SCONJ
fcis-10203	59	24	the	the	DET
fcis-10203	59	25	tasks	task	NOUN
fcis-10203	59	26	do	do	AUX
fcis-10203	59	27	n’t	not	PART
fcis-10203	59	28	interact	interact	VERB
fcis-10203	59	29	and	and	CCONJ
fcis-10203	59	30	the	the	DET
fcis-10203	59	31	shared	share	VERB
fcis-10203	59	32	parameters	parameter	NOUN
fcis-10203	59	33	are	be	AUX
fcis-10203	59	34	fixed	fix	VERB
fcis-10203	59	35	,	,	PUNCT
fcis-10203	59	36	which	which	PRON
fcis-10203	59	37	indicates	indicate	VERB
fcis-10203	59	38	that	that	SCONJ
fcis-10203	59	39	the	the	DET
fcis-10203	59	40	model	model	NOUN
fcis-10203	59	41	can	can	AUX
fcis-10203	59	42	remember	remember	VERB
fcis-10203	59	43	previous	previous	ADJ
fcis-10203	59	44	tasks	task	NOUN
fcis-10203	59	45	perfectly	perfectly	ADV
fcis-10203	59	46	by	by	ADP
fcis-10203	59	47	using	use	VERB
fcis-10203	59	48	few	few	ADJ
fcis-10203	59	49	task	task	NOUN
fcis-10203	59	50	-	-	PUNCT
fcis-10203	59	51	specific	specific	ADJ
fcis-10203	59	52	parameters	parameter	NOUN
fcis-10203	59	53	.	.	PUNCT
fcis-10203	60	1	parameter	parameter	NOUN
fcis-10203	60	2	-	-	PUNCT
fcis-10203	60	3	efficient	efficient	ADJ
fcis-10203	60	4	prompt	prompt	ADJ
fcis-10203	60	5	tuning	tuning	NOUN
fcis-10203	60	6	(	(	PUNCT
fcis-10203	60	7	also	also	ADV
fcis-10203	60	8	called	call	VERB
fcis-10203	60	9	soft	soft	ADJ
fcis-10203	60	10	-	-	PUNCT
fcis-10203	60	11	prompt	prompt	ADJ
fcis-10203	60	12	tuning	tuning	NOUN
fcis-10203	60	13	)	)	PUNCT
fcis-10203	60	14	proposed	propose	VERB
fcis-10203	60	15	the	the	DET
fcis-10203	60	16	use	use	NOUN
fcis-10203	60	17	of	of	ADP
fcis-10203	60	18	adapter	adapter	NOUN
fcis-10203	60	19	modules	module	NOUN
fcis-10203	60	20	to	to	PART
fcis-10203	60	21	transfer	transfer	VERB
fcis-10203	60	22	,	,	PUNCT
fcis-10203	60	23	thereby	thereby	ADV
fcis-10203	60	24	creating	create	VERB
fcis-10203	60	25	a	a	DET
fcis-10203	60	26	compact	compact	ADJ
fcis-10203	60	27	and	and	CCONJ
fcis-10203	60	28	extensible	extensible	ADJ
fcis-10203	60	29	model	model	NOUN
fcis-10203	60	30	.	.	PUNCT
fcis-10203	61	1	only	only	ADV
fcis-10203	61	2	a	a	DET
fcis-10203	61	3	few	few	ADJ
fcis-10203	61	4	trainable	trainable	ADJ
fcis-10203	61	5	parameters	parameter	NOUN
fcis-10203	61	6	added	add	VERB
fcis-10203	61	7	in	in	ADP
fcis-10203	61	8	each	each	DET
fcis-10203	61	9	task	task	NOUN
fcis-10203	61	10	,	,	PUNCT
fcis-10203	61	11	and	and	CCONJ
fcis-10203	61	12	new	new	ADJ
fcis-10203	61	13	tasks	task	NOUN
fcis-10203	61	14	can	can	AUX
fcis-10203	61	15	be	be	AUX
fcis-10203	61	16	added	add	VERB
fcis-10203	61	17	without	without	ADP
fcis-10203	61	18	the	the	DET
fcis-10203	61	19	need	need	NOUN
fcis-10203	61	20	to	to	PART
fcis-10203	61	21	revisit	revisit	VERB
fcis-10203	61	22	previous	previous	ADJ
fcis-10203	61	23	ones	one	NOUN
fcis-10203	61	24	[	[	X
fcis-10203	61	25	19	19	NUM
fcis-10203	61	26	]	]	PUNCT
fcis-10203	61	27	.	.	PUNCT
fcis-10203	62	1	3	3	X
fcis-10203	62	2	.	.	X
fcis-10203	62	3	experiment	experiment	NOUN
fcis-10203	62	4	according	accord	VERB
fcis-10203	62	5	to	to	ADP
fcis-10203	62	6	the	the	DET
fcis-10203	62	7	paper	paper	NOUN
fcis-10203	62	8	of	of	ADP
fcis-10203	62	9	unifiedqa	unifiedqa	NOUN
fcis-10203	62	10	,	,	PUNCT
fcis-10203	62	11	its	its	PRON
fcis-10203	62	12	concept	concept	NOUN
fcis-10203	62	13	is	be	AUX
fcis-10203	62	14	suitable	suitable	ADJ
fcis-10203	62	15	for	for	ADP
fcis-10203	62	16	text	text	NOUN
fcis-10203	62	17	-	-	PUNCT
fcis-10203	62	18	to	to	ADP
fcis-10203	62	19	-	-	PUNCT
fcis-10203	62	20	text	text	NOUN
fcis-10203	62	21	encoding	encoding	NOUN
fcis-10203	62	22	and	and	CCONJ
fcis-10203	62	23	therefore	therefore	ADV
fcis-10203	62	24	used	use	VERB
fcis-10203	62	25	t5	t5	PROPN
fcis-10203	62	26	and	and	CCONJ
fcis-10203	62	27	bart	bart	PROPN
fcis-10203	62	28	to	to	PART
fcis-10203	62	29	reach	reach	VERB
fcis-10203	62	30	this	this	DET
fcis-10203	62	31	multi	multi	ADJ
fcis-10203	62	32	-	-	ADJ
fcis-10203	62	33	task	task	ADJ
fcis-10203	62	34	target	target	NOUN
fcis-10203	62	35	.	.	PUNCT
fcis-10203	63	1	unifiedqa	unifiedqa	PROPN
fcis-10203	63	2	eventually	eventually	ADV
fcis-10203	63	3	used	use	VERB
fcis-10203	63	4	t5	t5	PROPN
fcis-10203	63	5	-	-	PUNCT
fcis-10203	63	6	11b	11b	NOUN
fcis-10203	63	7	and	and	CCONJ
fcis-10203	63	8	bart	bart	NOUN
fcis-10203	63	9	-	-	PUNCT
fcis-10203	63	10	large	large	PROPN
fcis-10203	63	11	as	as	ADP
fcis-10203	63	12	the	the	DET
fcis-10203	63	13	starting	starting	NOUN
fcis-10203	63	14	point	point	NOUN
fcis-10203	63	15	to	to	ADP
fcis-10203	63	16	pretrain	pretrain	NOUN
fcis-10203	63	17	.	.	PUNCT
fcis-10203	64	1	for	for	ADP
fcis-10203	64	2	the	the	DET
fcis-10203	64	3	further	further	ADJ
fcis-10203	64	4	research	research	NOUN
fcis-10203	64	5	,	,	PUNCT
fcis-10203	64	6	unifiedqa	unifiedqa	NOUN
fcis-10203	64	7	-	-	PUNCT
fcis-10203	64	8	v2	v2	NOUN
fcis-10203	64	9	is	be	AUX
fcis-10203	64	10	trained	train	VERB
fcis-10203	64	11	on	on	ADP
fcis-10203	64	12	20	20	NUM
fcis-10203	64	13	datasets	dataset	NOUN
fcis-10203	64	14	while	while	SCONJ
fcis-10203	64	15	unifiedqa	unifiedqa	ADJ
fcis-10203	64	16	is	be	AUX
fcis-10203	64	17	trained	train	VERB
fcis-10203	64	18	on	on	ADP
fcis-10203	64	19	8	8	NUM
fcis-10203	64	20	datasets	dataset	NOUN
fcis-10203	64	21	.	.	PUNCT
fcis-10203	65	1	in	in	ADP
fcis-10203	65	2	addition	addition	NOUN
fcis-10203	65	3	,	,	PUNCT
fcis-10203	65	4	unifiedqa	unifiedqa	NOUN
fcis-10203	65	5	-	-	PUNCT
fcis-10203	65	6	v2	v2	NOUN
fcis-10203	65	7	is	be	AUX
fcis-10203	65	8	trained	train	VERB
fcis-10203	65	9	for	for	ADP
fcis-10203	65	10	350k	350k	ADJ
fcis-10203	65	11	steps	step	NOUN
fcis-10203	65	12	and	and	CCONJ
fcis-10203	65	13	unifiedqa	unifiedqa	NOUN
fcis-10203	65	14	is	be	AUX
fcis-10203	65	15	trained	train	VERB
fcis-10203	65	16	for	for	ADP
fcis-10203	65	17	100k	100k	NUM
fcis-10203	65	18	steps	step	NOUN
fcis-10203	65	19	[	[	PUNCT
fcis-10203	65	20	13	13	NUM
fcis-10203	65	21	-	-	SYM
fcis-10203	65	22	14	14	NUM
fcis-10203	65	23	]	]	PUNCT
fcis-10203	65	24	.	.	PUNCT
fcis-10203	66	1	in	in	ADP
fcis-10203	66	2	this	this	DET
fcis-10203	66	3	paper	paper	NOUN
fcis-10203	66	4	,	,	PUNCT
fcis-10203	66	5	i	i	PRON
fcis-10203	66	6	also	also	ADV
fcis-10203	66	7	use	use	VERB
fcis-10203	66	8	t5	t5	PROPN
fcis-10203	66	9	as	as	ADP
fcis-10203	66	10	the	the	DET
fcis-10203	66	11	starting	starting	NOUN
fcis-10203	66	12	point	point	NOUN
fcis-10203	66	13	to	to	ADP
fcis-10203	66	14	pretrain	pretrain	NOUN
fcis-10203	66	15	.	.	PUNCT
fcis-10203	67	1	firstly	firstly	ADV
fcis-10203	67	2	,	,	PUNCT
fcis-10203	67	3	i	i	PRON
fcis-10203	67	4	collect	collect	VERB
fcis-10203	67	5	,	,	PUNCT
fcis-10203	67	6	generate	generate	VERB
fcis-10203	67	7	and	and	CCONJ
fcis-10203	67	8	pre	pre	ADJ
fcis-10203	67	9	-	-	NOUN
fcis-10203	67	10	process	process	NOUN
fcis-10203	67	11	some	some	DET
fcis-10203	67	12	situation	situation	NOUN
fcis-10203	67	13	puzzle	puzzle	NOUN
fcis-10203	67	14	data	datum	NOUN
fcis-10203	67	15	and	and	CCONJ
fcis-10203	67	16	put	put	VERB
fcis-10203	67	17	them	they	PRON
fcis-10203	67	18	into	into	ADP
fcis-10203	67	19	the	the	DET
fcis-10203	67	20	situation	situation	NOUN
fcis-10203	67	21	puzzle	puzzle	NOUN
fcis-10203	67	22	dataset	dataset	NOUN
fcis-10203	67	23	(	(	PUNCT
fcis-10203	67	24	spq	spq	NOUN
fcis-10203	67	25	)	)	PUNCT
fcis-10203	67	26	,	,	PUNCT
fcis-10203	67	27	and	and	CCONJ
fcis-10203	67	28	then	then	ADV
fcis-10203	67	29	train	train	VERB
fcis-10203	67	30	20	20	NUM
fcis-10203	67	31	unifiedqa	unifiedqa	ADJ
fcis-10203	67	32	-	-	PUNCT
fcis-10203	67	33	v2	v2	NOUN
fcis-10203	67	34	datasets	dataset	NOUN
fcis-10203	67	35	+	+	CCONJ
fcis-10203	67	36	spq	spq	NOUN
fcis-10203	67	37	dataset	dataset	NOUN
fcis-10203	67	38	in	in	ADP
fcis-10203	67	39	the	the	DET
fcis-10203	67	40	model	model	NOUN
fcis-10203	67	41	.	.	PUNCT
fcis-10203	68	1	in	in	ADP
fcis-10203	68	2	the	the	DET
fcis-10203	68	3	end	end	NOUN
fcis-10203	68	4	,	,	PUNCT
fcis-10203	68	5	i	i	PRON
fcis-10203	68	6	fine	fine	ADJ
fcis-10203	68	7	-	-	PUNCT
fcis-10203	68	8	tune	tune	NOUN
fcis-10203	68	9	them	they	PRON
fcis-10203	68	10	by	by	ADP
fcis-10203	68	11	tpu	tpu	NOUN
fcis-10203	68	12	and	and	CCONJ
fcis-10203	68	13	prompttune	prompttune	VERB
fcis-10203	68	14	them	they	PRON
fcis-10203	68	15	by	by	ADP
fcis-10203	68	16	gpu	gpu	PROPN
fcis-10203	68	17	and	and	CCONJ
fcis-10203	68	18	discuss	discuss	VERB
fcis-10203	68	19	the	the	DET
fcis-10203	68	20	differences	difference	NOUN
fcis-10203	68	21	between	between	ADP
fcis-10203	68	22	the	the	DET
fcis-10203	68	23	spqa	spqa	NOUN
fcis-10203	68	24	and	and	CCONJ
fcis-10203	68	25	unifiedqa	unifiedqa	NOUN
fcis-10203	68	26	-	-	PUNCT
fcis-10203	68	27	v2	v2	NOUN
fcis-10203	68	28	.	.	PUNCT
fcis-10203	68	29	3.1	3.1	NUM
fcis-10203	68	30	.	.	PUNCT
fcis-10203	68	31	situation	situation	NOUN
fcis-10203	68	32	puzzle	puzzle	NOUN
fcis-10203	68	33	dataset	dataset	NOUN
fcis-10203	68	34	table	table	NOUN
fcis-10203	68	35	1	1	NUM
fcis-10203	68	36	.	.	PUNCT
fcis-10203	69	1	situation	situation	NOUN
fcis-10203	69	2	puzzle	puzzle	NOUN
fcis-10203	69	3	dataset	dataset	VERB
fcis-10203	69	4	example	example	NOUN
fcis-10203	69	5	questions	question	NOUN
fcis-10203	69	6	answers	answer	NOUN
fcis-10203	69	7	am	be	AUX
fcis-10203	69	8	i	i	PRON
fcis-10203	69	9	a	a	DET
fcis-10203	69	10	tramp	tramp	NOUN
fcis-10203	70	1	and	and	CCONJ
fcis-10203	70	2	i	i	PRON
fcis-10203	70	3	will	will	AUX
fcis-10203	70	4	die	die	VERB
fcis-10203	70	5	because	because	SCONJ
fcis-10203	70	6	of	of	ADP
fcis-10203	70	7	the	the	DET
fcis-10203	70	8	cold	cold	NOUN
fcis-10203	70	9	?	?	PUNCT
fcis-10203	71	1	no	no	PRON
fcis-10203	71	2	is	be	AUX
fcis-10203	71	3	my	my	PRON
fcis-10203	71	4	coming	come	VERB
fcis-10203	71	5	death	death	NOUN
fcis-10203	71	6	related	relate	VERB
fcis-10203	71	7	to	to	ADP
fcis-10203	71	8	my	my	PRON
fcis-10203	71	9	career	career	NOUN
fcis-10203	71	10	?	?	PUNCT
fcis-10203	72	1	yes	yes	INTJ
fcis-10203	72	2	are	be	AUX
fcis-10203	72	3	my	my	PRON
fcis-10203	72	4	pants	pant	NOUN
fcis-10203	72	5	different	different	ADJ
fcis-10203	72	6	from	from	ADP
fcis-10203	72	7	the	the	DET
fcis-10203	72	8	ordinary	ordinary	ADJ
fcis-10203	72	9	pants	pant	NOUN
fcis-10203	72	10	?	?	PUNCT
fcis-10203	73	1	yes	yes	INTJ
fcis-10203	73	2	are	be	AUX
fcis-10203	73	3	my	my	PRON
fcis-10203	73	4	pants	pant	NOUN
fcis-10203	73	5	related	relate	VERB
fcis-10203	73	6	to	to	ADP
fcis-10203	73	7	my	my	PRON
fcis-10203	73	8	career	career	NOUN
fcis-10203	73	9	?	?	PUNCT
fcis-10203	74	1	yes	yes	INTJ
fcis-10203	74	2	are	be	AUX
fcis-10203	74	3	my	my	PRON
fcis-10203	74	4	pants	pant	NOUN
fcis-10203	74	5	used	use	VERB
fcis-10203	74	6	to	to	PART
fcis-10203	74	7	ensure	ensure	VERB
fcis-10203	74	8	my	my	PRON
fcis-10203	74	9	safety	safety	NOUN
fcis-10203	74	10	?	?	PUNCT
fcis-10203	75	1	yes	yes	INTJ
fcis-10203	75	2	is	be	AUX
fcis-10203	75	3	my	my	PRON
fcis-10203	75	4	career	career	NOUN
fcis-10203	75	5	about	about	ADP
fcis-10203	75	6	underwater	underwater	ADJ
fcis-10203	75	7	works	work	NOUN
fcis-10203	75	8	?	?	PUNCT
fcis-10203	76	1	no	no	PRON
fcis-10203	76	2	is	be	AUX
fcis-10203	76	3	my	my	PRON
fcis-10203	76	4	career	career	NOUN
fcis-10203	76	5	about	about	ADP
fcis-10203	76	6	underground	underground	ADJ
fcis-10203	76	7	works	work	NOUN
fcis-10203	76	8	?	?	PUNCT
fcis-10203	77	1	no	no	PRON
fcis-10203	77	2	is	be	AUX
fcis-10203	77	3	my	my	PRON
fcis-10203	77	4	career	career	NOUN
fcis-10203	77	5	about	about	ADP
fcis-10203	77	6	ground	ground	NOUN
fcis-10203	77	7	works	work	NOUN
fcis-10203	77	8	?	?	PUNCT
fcis-10203	78	1	no	no	PRON
fcis-10203	78	2	is	be	AUX
fcis-10203	78	3	my	my	PRON
fcis-10203	78	4	career	career	NOUN
fcis-10203	78	5	about	about	ADP
fcis-10203	78	6	high	high	ADJ
fcis-10203	78	7	altitude	altitude	NOUN
fcis-10203	78	8	works	work	NOUN
fcis-10203	78	9	?	?	PUNCT
fcis-10203	79	1	no	no	PRON
fcis-10203	79	2	is	be	AUX
fcis-10203	79	3	my	my	PRON
fcis-10203	79	4	career	career	NOUN
fcis-10203	79	5	about	about	ADP
fcis-10203	79	6	space	space	NOUN
fcis-10203	79	7	works	work	NOUN
fcis-10203	79	8	?	?	PUNCT
fcis-10203	80	1	yes	yes	INTJ
fcis-10203	80	2	in	in	ADP
fcis-10203	80	3	this	this	DET
fcis-10203	80	4	paper	paper	NOUN
fcis-10203	80	5	,	,	PUNCT
fcis-10203	80	6	i	i	PRON
fcis-10203	80	7	added	add	VERB
fcis-10203	80	8	some	some	DET
fcis-10203	80	9	datasets	dataset	NOUN
fcis-10203	80	10	about	about	ADP
fcis-10203	80	11	situation	situation	NOUN
fcis-10203	80	12	puzzle	puzzle	NOUN
fcis-10203	80	13	.	.	PUNCT
fcis-10203	81	1	take	take	VERB
fcis-10203	81	2	an	an	DET
fcis-10203	81	3	example	example	NOUN
fcis-10203	81	4	,	,	PUNCT
fcis-10203	81	5	the	the	DET
fcis-10203	81	6	contents	content	NOUN
fcis-10203	81	7	of	of	ADP
fcis-10203	81	8	the	the	DET
fcis-10203	81	9	story	story	NOUN
fcis-10203	81	10	are	be	AUX
fcis-10203	81	11	:	:	PUNCT
fcis-10203	81	12	“	"	PUNCT
fcis-10203	81	13	my	my	PRON
fcis-10203	81	14	pants	pant	NOUN
fcis-10203	81	15	are	be	AUX
fcis-10203	81	16	torn	tear	VERB
fcis-10203	81	17	,	,	PUNCT
fcis-10203	81	18	i	i	PRON
fcis-10203	81	19	know	know	VERB
fcis-10203	81	20	i	i	PRON
fcis-10203	81	21	'm	be	AUX
fcis-10203	81	22	going	go	VERB
fcis-10203	81	23	to	to	PART
fcis-10203	81	24	die	die	VERB
fcis-10203	81	25	soon	soon	ADV
fcis-10203	81	26	.	.	PUNCT
fcis-10203	82	1	because	because	SCONJ
fcis-10203	82	2	i	i	PRON
fcis-10203	82	3	am	be	AUX
fcis-10203	82	4	an	an	DET
fcis-10203	82	5	astronaut	astronaut	NOUN
fcis-10203	82	6	.	.	PUNCT
fcis-10203	83	1	one	one	NUM
fcis-10203	83	2	day	day	NOUN
fcis-10203	83	3	,	,	PUNCT
fcis-10203	83	4	i	i	PRON
fcis-10203	83	5	was	be	AUX
fcis-10203	83	6	carrying	carry	VERB
fcis-10203	83	7	out	out	ADP
fcis-10203	83	8	a	a	DET
fcis-10203	83	9	mission	mission	NOUN
fcis-10203	83	10	in	in	ADP
fcis-10203	83	11	space	space	NOUN
fcis-10203	83	12	wearing	wear	VERB
fcis-10203	83	13	a	a	DET
fcis-10203	83	14	spacesuit	spacesuit	NOUN
fcis-10203	83	15	when	when	SCONJ
fcis-10203	83	16	i	i	PRON
fcis-10203	83	17	suddenly	suddenly	ADV
fcis-10203	83	18	noticed	notice	VERB
fcis-10203	83	19	that	that	SCONJ
fcis-10203	83	20	my	my	PRON
fcis-10203	83	21	pants	pant	NOUN
fcis-10203	83	22	were	be	AUX
fcis-10203	83	23	torn	tear	VERB
fcis-10203	83	24	.	.	PUNCT
fcis-10203	84	1	afterwards	afterwards	ADV
fcis-10203	84	2	,	,	PUNCT
fcis-10203	84	3	i	i	PRON
fcis-10203	84	4	was	be	AUX
fcis-10203	84	5	exposed	expose	VERB
fcis-10203	84	6	to	to	ADP
fcis-10203	84	7	space	space	NOUN
fcis-10203	84	8	without	without	ADP
fcis-10203	84	9	air	air	NOUN
fcis-10203	84	10	pressure	pressure	NOUN
fcis-10203	84	11	and	and	CCONJ
fcis-10203	84	12	oxygen	oxygen	NOUN
fcis-10203	84	13	,	,	PUNCT
fcis-10203	84	14	and	and	CCONJ
fcis-10203	84	15	in	in	ADP
fcis-10203	84	16	less	less	ADJ
fcis-10203	84	17	than	than	ADP
fcis-10203	84	18	a	a	DET
fcis-10203	84	19	few	few	ADJ
fcis-10203	84	20	seconds	second	NOUN
fcis-10203	84	21	,	,	PUNCT
fcis-10203	84	22	i	i	PRON
fcis-10203	84	23	would	would	AUX
fcis-10203	84	24	die	die	VERB
fcis-10203	84	25	.	.	PUNCT
fcis-10203	84	26	”	"	PUNCT
fcis-10203	85	1	the	the	DET
fcis-10203	85	2	questions	question	NOUN
fcis-10203	85	3	and	and	CCONJ
fcis-10203	85	4	answers	answer	NOUN
fcis-10203	85	5	show	show	VERB
fcis-10203	85	6	in	in	ADP
fcis-10203	85	7	the	the	DET
fcis-10203	85	8	table	table	NOUN
fcis-10203	85	9	1	1	NUM
fcis-10203	85	10	.	.	NOUN
fcis-10203	85	11	3.2	3.2	NUM
fcis-10203	85	12	.	.	PUNCT
fcis-10203	86	1	training	training	NOUN
fcis-10203	86	2	models	model	NOUN
fcis-10203	86	3	and	and	CCONJ
fcis-10203	86	4	datasets	dataset	NOUN
fcis-10203	86	5	in	in	ADP
fcis-10203	86	6	this	this	DET
fcis-10203	86	7	paper	paper	NOUN
fcis-10203	86	8	,	,	PUNCT
fcis-10203	86	9	like	like	ADP
fcis-10203	86	10	the	the	DET
fcis-10203	86	11	construction	construction	NOUN
fcis-10203	86	12	of	of	ADP
fcis-10203	86	13	unifiedqa	unifiedqa	NOUN
fcis-10203	86	14	and	and	CCONJ
fcis-10203	86	15	unifiedqa	unifiedqa	NOUN
fcis-10203	86	16	-	-	PUNCT
fcis-10203	86	17	v2	v2	NOUN
fcis-10203	86	18	,	,	PUNCT
fcis-10203	86	19	i	i	PRON
fcis-10203	86	20	use	use	VERB
fcis-10203	86	21	the	the	DET
fcis-10203	86	22	t5	t5	PROPN
fcis-10203	86	23	architecture	architecture	NOUN
fcis-10203	86	24	(	(	PUNCT
fcis-10203	86	25	raffel	raffel	NOUN
fcis-10203	86	26	et	et	NOUN
fcis-10203	86	27	al	al	PROPN
fcis-10203	86	28	.	.	PROPN
fcis-10203	86	29	,	,	PUNCT
fcis-10203	86	30	2020	2020	NUM
fcis-10203	86	31	)	)	PUNCT
fcis-10203	86	32	for	for	ADP
fcis-10203	86	33	spqa	spqa	NOUN
fcis-10203	86	34	and	and	CCONJ
fcis-10203	86	35	train	train	VERB
fcis-10203	86	36	it	it	PRON
fcis-10203	86	37	for	for	ADP
fcis-10203	86	38	102k	102k	NUM
fcis-10203	86	39	.	.	PUNCT
fcis-10203	87	1	training	training	NOUN
fcis-10203	87	2	datasets	dataset	NOUN
fcis-10203	87	3	shows	show	VERB
fcis-10203	87	4	as	as	SCONJ
fcis-10203	87	5	follows	follow	VERB
fcis-10203	87	6	:	:	PUNCT
fcis-10203	87	7	spqa	spqa	NOUN
fcis-10203	87	8	(	(	PUNCT
fcis-10203	87	9	21	21	NUM
fcis-10203	87	10	datasets	dataset	NOUN
fcis-10203	87	11	):	):	PUNCT
fcis-10203	87	12	squad	squad	NOUN
fcis-10203	87	13	1.1	1.1	NUM
fcis-10203	87	14	,	,	PUNCT
fcis-10203	87	15	squad	squad	NOUN
fcis-10203	87	16	2	2	NUM
fcis-10203	87	17	,	,	PUNCT
fcis-10203	87	18	newsqa	newsqa	NOUN
fcis-10203	87	19	,	,	PUNCT
fcis-10203	87	20	quoref	quoref	NOUN
fcis-10203	87	21	,	,	PUNCT
fcis-10203	87	22	ropes	rope	NOUN
fcis-10203	87	23	,	,	PUNCT
fcis-10203	87	24	narrativeqa	narrativeqa	NOUN
fcis-10203	87	25	,	,	PUNCT
fcis-10203	87	26	drop	drop	NOUN
fcis-10203	87	27	,	,	PUNCT
fcis-10203	87	28	naturalquestions	naturalquestion	NOUN
fcis-10203	87	29	,	,	PUNCT
fcis-10203	87	30	mctest	mct	ADJ
fcis-10203	87	31	,	,	PUNCT
fcis-10203	87	32	race	race	NOUN
fcis-10203	87	33	,	,	PUNCT
fcis-10203	87	34	openbookqa	openbookqa	NOUN
fcis-10203	87	35	,	,	PUNCT
fcis-10203	87	36	arc	arc	NOUN
fcis-10203	87	37	,	,	PUNCT
fcis-10203	87	38	commonsenseqa	commonsenseqa	PROPN
fcis-10203	87	39	,	,	PUNCT
fcis-10203	87	40	qasc	qasc	PROPN
fcis-10203	87	41	,	,	PUNCT
fcis-10203	87	42	physicaliqa	physicaliqa	PROPN
fcis-10203	87	43	,	,	PUNCT
fcis-10203	87	44	socialiqa	socialiqa	NOUN
fcis-10203	87	45	,	,	PUNCT
fcis-10203	87	46	winogrande	winogrande	NOUN
fcis-10203	87	47	,	,	PUNCT
fcis-10203	87	48	boolq	boolq	ADV
fcis-10203	87	49	,	,	PUNCT
fcis-10203	87	50	multirc	multirc	X
fcis-10203	87	51	(	(	PUNCT
fcis-10203	87	52	yes	yes	INTJ
fcis-10203	87	53	/	/	SYM
fcis-10203	87	54	no	no	NOUN
fcis-10203	87	55	)	)	PUNCT
fcis-10203	87	56	,	,	PUNCT
fcis-10203	87	57	boolq	boolq	NOUN
fcis-10203	87	58	-	-	PUNCT
fcis-10203	87	59	np	np	NOUN
fcis-10203	87	60	,	,	PUNCT
fcis-10203	87	61	spq	spq	NOUN
fcis-10203	87	62	.	.	PUNCT
fcis-10203	88	1	3.3	3.3	NUM
fcis-10203	88	2	.	.	PUNCT
fcis-10203	89	1	details	detail	NOUN
fcis-10203	89	2	on	on	ADP
fcis-10203	89	3	the	the	DET
fcis-10203	89	4	experiments	experiment	NOUN
fcis-10203	89	5	some	some	DET
fcis-10203	89	6	details	detail	NOUN
fcis-10203	89	7	on	on	ADP
fcis-10203	89	8	the	the	DET
fcis-10203	89	9	experiments	experiment	NOUN
fcis-10203	89	10	shows	show	VERB
fcis-10203	89	11	as	as	SCONJ
fcis-10203	89	12	follows	follow	VERB
fcis-10203	89	13	:	:	PUNCT
fcis-10203	89	14	(	(	PUNCT
fcis-10203	89	15	1	1	X
fcis-10203	89	16	)	)	PUNCT
fcis-10203	89	17	models	model	NOUN
fcis-10203	89	18	:	:	PUNCT
fcis-10203	89	19	t5(3b	t5(3b	ADJ
fcis-10203	89	20	-	-	PUNCT
fcis-10203	89	21	tpu	tpu	NOUN
fcis-10203	89	22	)	)	PUNCT
fcis-10203	89	23	and	and	CCONJ
fcis-10203	89	24	t5(gpu	t5(gpu	NOUN
fcis-10203	89	25	)	)	PUNCT
fcis-10203	89	26	.	.	PUNCT
fcis-10203	90	1	(	(	PUNCT
fcis-10203	90	2	2	2	X
fcis-10203	90	3	)	)	PUNCT
fcis-10203	90	4	model	model	NOUN
fcis-10203	90	5	sizes	size	NOUN
fcis-10203	90	6	:	:	PUNCT
fcis-10203	90	7	mostly	mostly	ADV
fcis-10203	90	8	t5(3b	t5(3b	ADJ
fcis-10203	90	9	-	-	PUNCT
fcis-10203	90	10	tpu	tpu	NOUN
fcis-10203	90	11	)	)	PUNCT
fcis-10203	90	12	which	which	PRON
fcis-10203	90	13	has	have	VERB
fcis-10203	90	14	3	3	NUM
fcis-10203	90	15	billion	billion	NUM
fcis-10203	90	16	parameters	parameter	NOUN
fcis-10203	90	17	.	.	PUNCT
fcis-10203	91	1	(	(	PUNCT
fcis-10203	91	2	3	3	X
fcis-10203	91	3	)	)	PUNCT
fcis-10203	91	4	input	input	NOUN
fcis-10203	91	5	/	/	SYM
fcis-10203	91	6	output	output	NOUN
fcis-10203	91	7	size	size	NOUN
fcis-10203	91	8	:	:	PUNCT
fcis-10203	91	9	use	use	VERB
fcis-10203	91	10	token	token	NOUN
fcis-10203	91	11	-	-	PUNCT
fcis-10203	91	12	limits	limit	NOUN
fcis-10203	91	13	of	of	ADP
fcis-10203	91	14	size	size	NOUN
fcis-10203	91	15	512	512	NUM
fcis-10203	91	16	and	and	CCONJ
fcis-10203	91	17	100	100	NUM
fcis-10203	91	18	for	for	ADP
fcis-10203	91	19	inputs	input	NOUN
fcis-10203	91	20	and	and	CCONJ
fcis-10203	91	21	outputs	output	NOUN
fcis-10203	91	22	.	.	PUNCT
fcis-10203	92	1	(	(	PUNCT
fcis-10203	92	2	4	4	X
fcis-10203	92	3	)	)	PUNCT
fcis-10203	92	4	#	#	NOUN
fcis-10203	92	5	of	of	ADP
fcis-10203	92	6	iterations	iteration	NOUN
fcis-10203	92	7	for	for	ADP
fcis-10203	92	8	pretraining	pretraine	VERB
fcis-10203	92	9	on	on	ADP
fcis-10203	92	10	the	the	DET
fcis-10203	92	11	seed	seed	NOUN
fcis-10203	92	12	datasets	dataset	NOUN
fcis-10203	92	13	:	:	PUNCT
fcis-10203	92	14	all	all	DET
fcis-10203	92	15	models	model	NOUN
fcis-10203	92	16	are	be	AUX
fcis-10203	92	17	trained	train	VERB
fcis-10203	92	18	for	for	ADP
fcis-10203	92	19	102k	102k	NUM
fcis-10203	92	20	on	on	ADP
fcis-10203	92	21	the	the	DET
fcis-10203	92	22	seed	seed	NOUN
fcis-10203	92	23	datasets	dataset	NOUN
fcis-10203	92	24	.	.	PUNCT
fcis-10203	93	1	(	(	PUNCT
fcis-10203	93	2	5	5	X
fcis-10203	93	3	)	)	PUNCT
fcis-10203	93	4	learning	learn	VERB
fcis-10203	93	5	rates	rate	NOUN
fcis-10203	93	6	:	:	PUNCT
fcis-10203	93	7	use	use	VERB
fcis-10203	93	8	3e-3	3e-3	PRON
fcis-10203	93	9	for	for	ADP
fcis-10203	93	10	t5(3b	t5(3b	NOUN
fcis-10203	93	11	-	-	PUNCT
fcis-10203	93	12	tpu	tpu	NOUN
fcis-10203	93	13	)	)	PUNCT
fcis-10203	93	14	and	and	CCONJ
fcis-10203	93	15	t5(gpu	t5(gpu	NOUN
fcis-10203	93	16	)	)	PUNCT
fcis-10203	93	17	.	.	PUNCT
fcis-10203	94	1	(	(	PUNCT
fcis-10203	94	2	6	6	NUM
fcis-10203	94	3	)	)	PUNCT
fcis-10203	94	4	batch	batch	NOUN
fcis-10203	94	5	sizes	size	NOUN
fcis-10203	94	6	:	:	PUNCT
fcis-10203	94	7	use	use	VERB
fcis-10203	94	8	batches	batch	NOUN
fcis-10203	94	9	of	of	ADP
fcis-10203	94	10	16	16	NUM
fcis-10203	94	11	for	for	ADP
fcis-10203	94	12	the	the	DET
fcis-10203	94	13	t5(3b	t5(3b	NOUN
fcis-10203	94	14	-	-	PUNCT
fcis-10203	94	15	tpu	tpu	NOUN
fcis-10203	94	16	)	)	PUNCT
fcis-10203	94	17	and	and	CCONJ
fcis-10203	94	18	batches	batch	NOUN
fcis-10203	94	19	of	of	ADP
fcis-10203	94	20	2	2	NUM
fcis-10203	94	21	for	for	ADP
fcis-10203	94	22	t5(gpu	t5(gpu	NOUN
fcis-10203	94	23	)	)	PUNCT
fcis-10203	94	24	.	.	PUNCT
fcis-10203	95	1	(	(	PUNCT
fcis-10203	95	2	7	7	X
fcis-10203	95	3	)	)	PUNCT
fcis-10203	95	4	infrastructure	infrastructure	NOUN
fcis-10203	95	5	:	:	PUNCT
fcis-10203	95	6	use	use	VERB
fcis-10203	95	7	v2	v2	NOUN
fcis-10203	95	8	-	-	PUNCT
fcis-10203	95	9	8	8	NUM
fcis-10203	95	10	tpus	tpus	NOUN
fcis-10203	95	11	for	for	ADP
fcis-10203	95	12	t5(3b	t5(3b	NOUN
fcis-10203	95	13	-	-	PUNCT
fcis-10203	95	14	tpu	tpu	NOUN
fcis-10203	95	15	)	)	PUNCT
fcis-10203	95	16	models	model	NOUN
fcis-10203	95	17	and	and	CCONJ
fcis-10203	95	18	16	16	NUM
fcis-10203	95	19	g	g	NOUN
fcis-10203	95	20	gpus	gpu	NOUN
fcis-10203	95	21	for	for	ADP
fcis-10203	95	22	t5(gpu	t5(gpu	NOUN
fcis-10203	95	23	)	)	PUNCT
fcis-10203	95	24	models	model	NOUN
fcis-10203	95	25	.	.	PUNCT
fcis-10203	96	1	(	(	PUNCT
fcis-10203	96	2	8)	8)	NUM
fcis-10203	96	3	fine	fine	ADJ
fcis-10203	96	4	tuning	tuning	NOUN
fcis-10203	96	5	on	on	ADP
fcis-10203	96	6	datasets	dataset	NOUN
fcis-10203	96	7	:	:	PUNCT
fcis-10203	96	8	fine	fine	ADV
fcis-10203	96	9	-	-	PUNCT
fcis-10203	96	10	tuned	tune	VERB
fcis-10203	96	11	for	for	ADP
fcis-10203	96	12	102k	102k	NUM
fcis-10203	96	13	steps	step	NOUN
fcis-10203	96	14	and	and	CCONJ
fcis-10203	96	15	checkpoints	checkpoint	NOUN
fcis-10203	96	16	were	be	AUX
fcis-10203	96	17	saved	save	VERB
fcis-10203	96	18	per	per	ADP
fcis-10203	96	19	20k	20k	NOUN
fcis-10203	96	20	steps	step	NOUN
fcis-10203	96	21	.	.	PUNCT
fcis-10203	97	1	4	4	X
fcis-10203	97	2	.	.	X
fcis-10203	97	3	evaluation	evaluation	NOUN
fcis-10203	97	4	and	and	CCONJ
fcis-10203	97	5	results	result	NOUN
fcis-10203	97	6	in	in	ADP
fcis-10203	97	7	this	this	DET
fcis-10203	97	8	paper	paper	NOUN
fcis-10203	97	9	,	,	PUNCT
fcis-10203	97	10	i	i	PRON
fcis-10203	97	11	compared	compare	VERB
fcis-10203	97	12	the	the	DET
fcis-10203	97	13	spqa	spqa	NOUN
fcis-10203	97	14	with	with	ADP
fcis-10203	97	15	the	the	DET
fcis-10203	97	16	unifiedqav2	unifiedqav2	NOUN
fcis-10203	97	17	and	and	CCONJ
fcis-10203	97	18	evaluate	evaluate	VERB
fcis-10203	97	19	a	a	DET
fcis-10203	97	20	fixed	fix	VERB
fcis-10203	97	21	checkpoint	checkpoint	NOUN
fcis-10203	97	22	across	across	ADP
fcis-10203	97	23	the	the	DET
fcis-10203	97	24	target	target	NOUN
fcis-10203	97	25	datasets	dataset	NOUN
fcis-10203	97	26	:	:	PUNCT
fcis-10203	97	27	boolq	boolq	ADV
fcis-10203	97	28	,	,	PUNCT
fcis-10203	97	29	boolq	boolq	NOUN
fcis-10203	97	30	-	-	PUNCT
fcis-10203	97	31	np	np	NOUN
fcis-10203	97	32	,	,	PUNCT
fcis-10203	97	33	boolq	boolq	NOUN
fcis-10203	97	34	-	-	PUNCT
fcis-10203	97	35	cs	cs	NOUN
fcis-10203	97	36	(	(	PUNCT
fcis-10203	97	37	unseen	unseen	ADJ
fcis-10203	97	38	)	)	PUNCT
fcis-10203	97	39	,	,	PUNCT
fcis-10203	97	40	spq	spq	NOUN
fcis-10203	97	41	and	and	CCONJ
fcis-10203	97	42	spq	spq	NOUN
fcis-10203	97	43	-	-	PUNCT
fcis-10203	97	44	test	test	NOUN
fcis-10203	97	45	(	(	PUNCT
fcis-10203	97	46	unseen	unseen	ADV
fcis-10203	97	47	):	):	PUNCT
fcis-10203	97	48	both	both	PRON
fcis-10203	97	49	checkpoint	checkpoint	VERB
fcis-10203	97	50	100k	100k	NOUN
fcis-10203	97	51	for	for	ADP
fcis-10203	97	52	spqa	spqa	NOUN
fcis-10203	97	53	and	and	CCONJ
fcis-10203	97	54	unifiedqa	unifiedqa	NOUN
fcis-10203	97	55	-	-	PUNCT
fcis-10203	97	56	v2	v2	NOUN
fcis-10203	97	57	.	.	PUNCT
fcis-10203	98	1	in	in	ADP
fcis-10203	98	2	addition	addition	NOUN
fcis-10203	98	3	,	,	PUNCT
fcis-10203	98	4	i	i	PRON
fcis-10203	98	5	discuss	discuss	VERB
fcis-10203	98	6	the	the	DET
fcis-10203	98	7	prompt	prompt	ADJ
fcis-10203	98	8	tuning	tuning	NOUN
fcis-10203	98	9	between	between	ADP
fcis-10203	98	10	the	the	DET
fcis-10203	98	11	spqa	spqa	NOUN
fcis-10203	98	12	and	and	CCONJ
fcis-10203	98	13	unifiedqa	unifiedqa	NOUN
fcis-10203	98	14	-	-	PUNCT
fcis-10203	98	15	v2	v2	NOUN
fcis-10203	98	16	.	.	PUNCT
fcis-10203	99	1	eventually	eventually	ADV
fcis-10203	99	2	,	,	PUNCT
fcis-10203	99	3	i	i	PRON
fcis-10203	99	4	observe	observe	VERB
fcis-10203	99	5	the	the	DET
fcis-10203	99	6	characteristics	characteristic	NOUN
fcis-10203	99	7	65	65	NUM
fcis-10203	99	8	of	of	ADP
fcis-10203	99	9	the	the	DET
fcis-10203	99	10	answers	answer	NOUN
fcis-10203	99	11	given	give	VERB
fcis-10203	99	12	by	by	ADP
fcis-10203	99	13	the	the	DET
fcis-10203	99	14	spqa	spqa	NOUN
fcis-10203	99	15	,	,	PUNCT
fcis-10203	99	16	unifiedqa	unifiedqa	NOUN
fcis-10203	99	17	-	-	PUNCT
fcis-10203	99	18	v2	v2	NOUN
fcis-10203	99	19	and	and	CCONJ
fcis-10203	99	20	chatgpt	chatgpt	NOUN
fcis-10203	99	21	(	(	PUNCT
fcis-10203	99	22	gpt3.5	gpt3.5	NOUN
fcis-10203	99	23	)	)	PUNCT
fcis-10203	99	24	.	.	PUNCT
fcis-10203	100	1	evaluation	evaluation	NOUN
fcis-10203	100	2	datasets	dataset	NOUN
fcis-10203	100	3	shows	show	VERB
fcis-10203	100	4	as	as	SCONJ
fcis-10203	100	5	follows	follow	VERB
fcis-10203	100	6	:	:	PUNCT
fcis-10203	100	7	(	(	PUNCT
fcis-10203	100	8	1	1	X
fcis-10203	100	9	)	)	PUNCT
fcis-10203	100	10	boolq	boolq	NOUN
fcis-10203	100	11	(	(	PUNCT
fcis-10203	100	12	clark	clark	PROPN
fcis-10203	100	13	et	et	PROPN
fcis-10203	100	14	al	al	PROPN
fcis-10203	100	15	.	.	PROPN
fcis-10203	100	16	,	,	PUNCT
fcis-10203	100	17	2019	2019	NUM
fcis-10203	100	18	)	)	PUNCT
fcis-10203	100	19	,	,	PUNCT
fcis-10203	100	20	boolq	boolq	NOUN
fcis-10203	100	21	-	-	PUNCT
fcis-10203	100	22	np	np	INTJ
fcis-10203	100	23	(	(	PUNCT
fcis-10203	100	24	khashabi	khashabi	PROPN
fcis-10203	100	25	et	et	PROPN
fcis-10203	100	26	al	al	PROPN
fcis-10203	100	27	.	.	PROPN
fcis-10203	100	28	,	,	PUNCT
fcis-10203	100	29	2020a	2020a	NUM
fcis-10203	100	30	)	)	PUNCT
fcis-10203	100	31	the	the	DET
fcis-10203	100	32	binary	binary	NOUN
fcis-10203	100	33	(	(	PUNCT
fcis-10203	100	34	yes	yes	INTJ
fcis-10203	100	35	/	/	SYM
fcis-10203	100	36	no	no	NOUN
fcis-10203	100	37	)	)	PUNCT
fcis-10203	100	38	subset	subset	NOUN
fcis-10203	100	39	of	of	ADP
fcis-10203	100	40	multirc	multirc	PROPN
fcis-10203	100	41	(	(	PUNCT
fcis-10203	100	42	khashabi	khashabi	PROPN
fcis-10203	100	43	et	et	PROPN
fcis-10203	100	44	al	al	PROPN
fcis-10203	100	45	.	.	PROPN
fcis-10203	100	46	,	,	PUNCT
fcis-10203	100	47	2018	2018	NUM
fcis-10203	100	48	)	)	PUNCT
fcis-10203	100	49	(	(	PUNCT
fcis-10203	100	50	2	2	X
fcis-10203	100	51	)	)	PUNCT
fcis-10203	100	52	boolq	boolq	NOUN
fcis-10203	100	53	-	-	PUNCT
fcis-10203	100	54	cs	cs	PROPN
fcis-10203	100	55	(	(	PUNCT
fcis-10203	100	56	unseen	unseen	ADJ
fcis-10203	100	57	)	)	PUNCT
fcis-10203	100	58	from	from	ADP
fcis-10203	100	59	strategyqa	strategyqa	ADJ
fcis-10203	100	60	(	(	PUNCT
fcis-10203	100	61	geva	geva	PROPN
fcis-10203	100	62	et	et	PROPN
fcis-10203	100	63	al	al	PROPN
fcis-10203	100	64	.	.	PROPN
fcis-10203	100	65	,	,	PUNCT
fcis-10203	100	66	2021	2021	NUM
fcis-10203	100	67	)	)	PUNCT
fcis-10203	100	68	and	and	CCONJ
fcis-10203	100	69	pubmedqa	pubmedqa	ADJ
fcis-10203	100	70	(	(	PUNCT
fcis-10203	100	71	jin	jin	NOUN
fcis-10203	100	72	et	et	PROPN
fcis-10203	100	73	al	al	PROPN
fcis-10203	100	74	.	.	PROPN
fcis-10203	100	75	,	,	PUNCT
fcis-10203	100	76	2019	2019	NUM
fcis-10203	100	77	)	)	PUNCT
fcis-10203	100	78	.	.	PUNCT
fcis-10203	101	1	4.1	4.1	NUM
fcis-10203	101	2	.	.	PUNCT
fcis-10203	101	3	metrics	metric	NOUN
fcis-10203	101	4	i	i	PRON
fcis-10203	101	5	evaluate	evaluate	VERB
fcis-10203	101	6	each	each	DET
fcis-10203	101	7	dataset	dataset	NOUN
fcis-10203	101	8	via	via	ADP
fcis-10203	101	9	their	their	PRON
fcis-10203	101	10	common	common	ADJ
fcis-10203	101	11	metric	metric	NOUN
fcis-10203	101	12	by	by	ADP
fcis-10203	101	13	the	the	DET
fcis-10203	101	14	accuracy	accuracy	NOUN
fcis-10203	101	15	.	.	PUNCT
fcis-10203	102	1	for	for	ADP
fcis-10203	102	2	yes	yes	PROPN
fcis-10203	102	3	/	/	SYM
fcis-10203	102	4	no	no	DET
fcis-10203	102	5	questions	question	NOUN
fcis-10203	102	6	,	,	PUNCT
fcis-10203	102	7	if	if	SCONJ
fcis-10203	102	8	the	the	DET
fcis-10203	102	9	model	model	NOUN
fcis-10203	102	10	gives	give	VERB
fcis-10203	102	11	the	the	DET
fcis-10203	102	12	correct	correct	ADJ
fcis-10203	102	13	answer	answer	NOUN
fcis-10203	102	14	(	(	PUNCT
fcis-10203	102	15	“	"	PUNCT
fcis-10203	102	16	yes	yes	INTJ
fcis-10203	102	17	”	"	PUNCT
fcis-10203	102	18	or	or	CCONJ
fcis-10203	102	19	“	"	PUNCT
fcis-10203	102	20	yes	yes	INTJ
fcis-10203	102	21	,	,	PUNCT
fcis-10203	102	22	it	it	PRON
fcis-10203	102	23	is	be	AUX
fcis-10203	102	24	right	right	ADJ
fcis-10203	102	25	.	.	PUNCT
fcis-10203	102	26	”	"	PUNCT
fcis-10203	102	27	)	)	PUNCT
fcis-10203	102	28	,	,	PUNCT
fcis-10203	102	29	it	it	PRON
fcis-10203	102	30	gains	gain	VERB
fcis-10203	102	31	one	one	NUM
fcis-10203	102	32	score	score	NOUN
fcis-10203	102	33	.	.	PUNCT
fcis-10203	103	1	otherwise	otherwise	ADV
fcis-10203	103	2	,	,	PUNCT
fcis-10203	103	3	it	it	PRON
fcis-10203	103	4	gains	gain	VERB
fcis-10203	103	5	no	no	DET
fcis-10203	103	6	score	score	NOUN
fcis-10203	103	7	.	.	PUNCT
fcis-10203	104	1	in	in	ADP
fcis-10203	104	2	addition	addition	NOUN
fcis-10203	104	3	,	,	PUNCT
fcis-10203	104	4	i	i	PRON
fcis-10203	104	5	also	also	ADV
fcis-10203	104	6	provide	provide	VERB
fcis-10203	104	7	“	"	PUNCT
fcis-10203	104	8	aggregate	aggregate	ADJ
fcis-10203	104	9	scores	score	NOUN
fcis-10203	104	10	”	"	PUNCT
fcis-10203	104	11	that	that	PRON
fcis-10203	104	12	compare	compare	VERB
fcis-10203	104	13	the	the	DET
fcis-10203	104	14	two	two	NUM
fcis-10203	104	15	models	model	NOUN
fcis-10203	104	16	.	.	PUNCT
fcis-10203	105	1	for	for	ADP
fcis-10203	105	2	“	"	PUNCT
fcis-10203	105	3	aggregate	aggregate	ADJ
fcis-10203	105	4	scores	score	NOUN
fcis-10203	105	5	”	"	PUNCT
fcis-10203	105	6	,	,	PUNCT
fcis-10203	105	7	it	it	PRON
fcis-10203	105	8	gives	give	VERB
fcis-10203	105	9	two	two	NUM
fcis-10203	105	10	metrics	metric	NOUN
fcis-10203	105	11	:	:	PUNCT
fcis-10203	105	12	1.the	1.the	DET
fcis-10203	105	13	difference	difference	NOUN
fcis-10203	105	14	between	between	ADP
fcis-10203	105	15	the	the	DET
fcis-10203	105	16	average	average	ADJ
fcis-10203	105	17	performance	performance	NOUN
fcis-10203	105	18	score	score	NOUN
fcis-10203	105	19	of	of	ADP
fcis-10203	105	20	spqa	spqa	NOUN
fcis-10203	105	21	and	and	CCONJ
fcis-10203	105	22	unifiedqa	unifiedqa	ADJ
fcis-10203	105	23	-	-	PUNCT
fcis-10203	105	24	v2	v2	NOUN
fcis-10203	105	25	models	model	NOUN
fcis-10203	105	26	of	of	ADP
fcis-10203	105	27	the	the	DET
fcis-10203	105	28	same	same	ADJ
fcis-10203	105	29	size	size	NOUN
fcis-10203	105	30	(	(	PUNCT
fcis-10203	105	31	indicated	indicate	VERB
fcis-10203	105	32	with	with	ADP
fcis-10203	105	33	‘	'	PUNCT
fcis-10203	105	34	sp	sp	NOUN
fcis-10203	105	35	–	–	PUNCT
fcis-10203	105	36	uni2	uni2	NOUN
fcis-10203	105	37	’	'	PUNCT
fcis-10203	105	38	)	)	PUNCT
fcis-10203	105	39	;	;	PUNCT
fcis-10203	105	40	2.the	2.the	DET
fcis-10203	105	41	percentage	percentage	NOUN
fcis-10203	105	42	that	that	PRON
fcis-10203	105	43	spqa	spqa	AUX
fcis-10203	105	44	causes	cause	VERB
fcis-10203	105	45	a	a	DET
fcis-10203	105	46	better	well	ADJ
fcis-10203	105	47	performance	performance	NOUN
fcis-10203	105	48	than	than	ADP
fcis-10203	105	49	unifiedqa	unifiedqa	NOUN
fcis-10203	105	50	-	-	PUNCT
fcis-10203	105	51	v2	v2	NOUN
fcis-10203	105	52	of	of	ADP
fcis-10203	105	53	the	the	DET
fcis-10203	105	54	same	same	ADJ
fcis-10203	105	55	size	size	NOUN
fcis-10203	105	56	(	(	PUNCT
fcis-10203	105	57	indicated	indicate	VERB
fcis-10203	105	58	with	with	ADP
fcis-10203	105	59	‘	'	PUNCT
fcis-10203	105	60	sp	sp	ADP
fcis-10203	105	61	uni2	uni2	NOUN
fcis-10203	105	62	?	?	PUNCT
fcis-10203	105	63	’	'	PUNCT
fcis-10203	105	64	)	)	PUNCT
fcis-10203	106	1	[	[	X
fcis-10203	106	2	14	14	NUM
fcis-10203	106	3	]	]	SYM
fcis-10203	106	4	.	.	PUNCT
fcis-10203	107	1	4.2	4.2	NUM
fcis-10203	107	2	.	.	PUNCT
fcis-10203	107	3	evaluation	evaluation	NOUN
fcis-10203	107	4	i	i	PRON
fcis-10203	107	5	evaluate	evaluate	VERB
fcis-10203	107	6	each	each	DET
fcis-10203	107	7	bool	bool	NOUN
fcis-10203	107	8	qa	qa	PROPN
fcis-10203	107	9	dataset	dataset	VERB
fcis-10203	107	10	via	via	ADP
fcis-10203	107	11	their	their	PRON
fcis-10203	107	12	common	common	ADJ
fcis-10203	107	13	metric	metric	ADJ
fcis-10203	107	14	.	.	PUNCT
fcis-10203	108	1	table	table	NOUN
fcis-10203	108	2	2	2	NUM
fcis-10203	108	3	.	.	X
fcis-10203	108	4	evaluation	evaluation	NOUN
fcis-10203	108	5	between	between	ADP
fcis-10203	108	6	unifiedqa	unifiedqa	NOUN
fcis-10203	108	7	-	-	PUNCT
fcis-10203	108	8	v2	v2	NOUN
fcis-10203	108	9	and	and	CCONJ
fcis-10203	108	10	spqa	spqa	NOUN
fcis-10203	108	11	with	with	ADP
fcis-10203	108	12	fine	fine	ADV
fcis-10203	108	13	-	-	PUNCT
fcis-10203	108	14	tuning	tune	VERB
fcis-10203	108	15	boolq	boolq	NOUN
fcis-10203	108	16	-	-	PUNCT
fcis-10203	108	17	dev	dev	NOUN
fcis-10203	108	18	boolq	boolq	NOUN
fcis-10203	108	19	-	-	PUNCT
fcis-10203	108	20	np	np	NOUN
fcis-10203	108	21	-	-	PUNCT
fcis-10203	108	22	dev	dev	NOUN
fcis-10203	108	23	boolq	boolq	NOUN
fcis-10203	108	24	-	-	PUNCT
fcis-10203	108	25	cs	cs	PROPN
fcis-10203	108	26	-	-	PUNCT
fcis-10203	108	27	dev(unseen	dev(unseen	ADJ
fcis-10203	108	28	)	)	PUNCT
fcis-10203	108	29	spq	spq	NOUN
fcis-10203	108	30	-	-	PUNCT
fcis-10203	108	31	dev	dev	NOUN
fcis-10203	108	32	spq	spq	NOUN
fcis-10203	108	33	-	-	PUNCT
fcis-10203	108	34	test(unseen	test(unseen	PROPN
fcis-10203	108	35	)	)	PUNCT
fcis-10203	108	36	unifiedqa	unifiedqa	NOUN
fcis-10203	108	37	-	-	PUNCT
fcis-10203	108	38	v2	v2	NOUN
fcis-10203	108	39	spqa	spqa	ADJ
fcis-10203	108	40	unifiedqa	unifiedqa	NOUN
fcis-10203	108	41	-	-	PUNCT
fcis-10203	108	42	v2	v2	NOUN
fcis-10203	108	43	spqa	spqa	ADJ
fcis-10203	108	44	unifiedqa	unifiedqa	NOUN
fcis-10203	108	45	-	-	PUNCT
fcis-10203	108	46	v2	v2	NOUN
fcis-10203	108	47	spqa	spqa	ADJ
fcis-10203	108	48	unifiedqa	unifiedqa	NOUN
fcis-10203	108	49	-	-	PUNCT
fcis-10203	108	50	v2	v2	NOUN
fcis-10203	108	51	spqa	spqa	ADJ
fcis-10203	108	52	unifiedqa	unifiedqa	NOUN
fcis-10203	108	53	-	-	PUNCT
fcis-10203	108	54	v2	v2	NOUN
fcis-10203	108	55	spqa	spqa	ADJ
fcis-10203	108	56	small	small	ADJ
fcis-10203	108	57	70.795	70.795	NUM
fcis-10203	108	58	70.306	70.306	NUM
fcis-10203	108	59	63.494	63.494	NUM
fcis-10203	108	60	59.795	59.795	NUM
fcis-10203	108	61	36.560	36.560	NUM
fcis-10203	108	62	34.553	34.553	NUM
fcis-10203	108	63	65.000	65.000	NUM
fcis-10203	108	64	65.000	65.000	NUM
fcis-10203	108	65	70.000	70.000	NUM
fcis-10203	108	66	75.000	75.000	NUM
fcis-10203	108	67	base	base	NOUN
fcis-10203	108	68	76.177	76.177	NUM
fcis-10203	108	69	78.960	78.960	NUM
fcis-10203	108	70	72.367	72.367	NUM
fcis-10203	108	71	73.486	73.486	NUM
fcis-10203	108	72	44.510	44.510	NUM
fcis-10203	108	73	46.084	46.084	NUM
fcis-10203	108	74	75.000	75.000	NUM
fcis-10203	108	75	85.000	85.000	NUM
fcis-10203	108	76	70.000	70.000	NUM
fcis-10203	108	77	85.000	85.000	NUM
fcis-10203	108	78	large	large	ADJ
fcis-10203	108	79	77.829	77.829	NUM
fcis-10203	108	80	81.865	81.865	NUM
fcis-10203	108	81	74.987	74.987	NUM
fcis-10203	108	82	76.764	76.764	NUM
fcis-10203	108	83	45.612	45.612	NUM
fcis-10203	108	84	47.580	47.580	NUM
fcis-10203	108	85	80.000	80.000	NUM
fcis-10203	108	86	85.000	85.000	NUM
fcis-10203	108	87	95.000	95.000	NUM
fcis-10203	108	88	95.000	95.000	NUM
fcis-10203	108	89	3b	3b	NUM
fcis-10203	108	90	86.606	86.606	NUM
fcis-10203	108	91	85.291	85.291	NUM
fcis-10203	108	92	83.149	83.149	NUM
fcis-10203	108	93	81.082	81.082	NUM
fcis-10203	108	94	49.587	49.587	NUM
fcis-10203	108	95	47.658	47.658	NUM
fcis-10203	108	96	85.000	85.000	NUM
fcis-10203	108	97	80.000	80.000	NUM
fcis-10203	108	98	100.000	100.000	NUM
fcis-10203	108	99	100.000	100.000	NUM
fcis-10203	108	100	table	table	NOUN
fcis-10203	108	101	3	3	NUM
fcis-10203	108	102	.	.	PUNCT
fcis-10203	109	1	aggregate	aggregate	ADJ
fcis-10203	109	2	scores	score	NOUN
fcis-10203	109	3	that	that	PRON
fcis-10203	109	4	contrast	contrast	VERB
fcis-10203	109	5	the	the	DET
fcis-10203	109	6	two	two	NUM
fcis-10203	109	7	models	model	NOUN
fcis-10203	109	8	with	with	ADP
fcis-10203	109	9	fine	fine	ADJ
fcis-10203	109	10	tuning	tuning	NOUN
fcis-10203	109	11	average	average	NOUN
fcis-10203	109	12	aggregated	aggregate	VERB
fcis-10203	109	13	unifiedqa	unifiedqa	ADV
fcis-10203	109	14	-	-	PUNCT
fcis-10203	109	15	v2	v2	NOUN
fcis-10203	109	16	spqa	spqa	NOUN
fcis-10203	109	17	sp	sp	ADP
fcis-10203	109	18	uni2	uni2	PROPN
fcis-10203	109	19	sp	sp	ADP
fcis-10203	109	20	uni2	uni2	PROPN
fcis-10203	109	21	?	?	PUNCT
fcis-10203	110	1	small	small	ADJ
fcis-10203	110	2	61.170	61.170	NUM
fcis-10203	110	3	60.931	60.931	NUM
fcis-10203	110	4	-0.239	-0.239	SYM
fcis-10203	110	5	33	33	NUM
fcis-10203	110	6	base	base	NOUN
fcis-10203	110	7	67.611	67.611	NUM
fcis-10203	110	8	73.706	73.706	NUM
fcis-10203	110	9	6.095	6.095	NUM
fcis-10203	110	10	100	100	NUM
fcis-10203	110	11	large	large	ADJ
fcis-10203	110	12	74.686	74.686	NUM
fcis-10203	110	13	77.242	77.242	NUM
fcis-10203	110	14	2.556	2.556	NUM
fcis-10203	110	15	100	100	NUM
fcis-10203	110	16	3b	3b	NUM
fcis-10203	110	17	80.868	80.868	NUM
fcis-10203	110	18	78.806	78.806	NUM
fcis-10203	110	19	-2.062	-2.062	SYM
fcis-10203	110	20	17	17	NUM
fcis-10203	110	21	table	table	NOUN
fcis-10203	110	22	4	4	NUM
fcis-10203	110	23	.	.	PUNCT
fcis-10203	110	24	evaluation	evaluation	NOUN
fcis-10203	110	25	between	between	ADP
fcis-10203	110	26	unifiedqa	unifiedqa	NOUN
fcis-10203	110	27	-	-	PUNCT
fcis-10203	110	28	v2	v2	NOUN
fcis-10203	110	29	and	and	CCONJ
fcis-10203	110	30	spqa	spqa	NOUN
fcis-10203	110	31	with	with	ADP
fcis-10203	110	32	prompt	prompt	NOUN
fcis-10203	110	33	-	-	PUNCT
fcis-10203	110	34	tuning	tune	VERB
fcis-10203	110	35	boolq	boolq	NOUN
fcis-10203	110	36	-	-	PUNCT
fcis-10203	110	37	dev	dev	NOUN
fcis-10203	110	38	boolq	boolq	NOUN
fcis-10203	110	39	-	-	PUNCT
fcis-10203	110	40	np	np	NOUN
fcis-10203	110	41	-	-	PUNCT
fcis-10203	110	42	dev	dev	NOUN
fcis-10203	110	43	boolq	boolq	NOUN
fcis-10203	110	44	-	-	PUNCT
fcis-10203	110	45	cs	cs	PROPN
fcis-10203	110	46	-	-	PUNCT
fcis-10203	110	47	dev(unseen	dev(unseen	ADJ
fcis-10203	110	48	)	)	PUNCT
fcis-10203	110	49	spq	spq	NOUN
fcis-10203	110	50	-	-	PUNCT
fcis-10203	110	51	dev	dev	NOUN
fcis-10203	110	52	spq	spq	NOUN
fcis-10203	110	53	-	-	PUNCT
fcis-10203	110	54	test(unseen	test(unseen	PROPN
fcis-10203	110	55	)	)	PUNCT
fcis-10203	110	56	prompt	prompt	ADJ
fcis-10203	110	57	unifiedqa	unifiedqa	ADJ
fcis-10203	110	58	-	-	PUNCT
fcis-10203	110	59	v2	v2	NOUN
fcis-10203	110	60	spqaprompt	spqaprompt	NOUN
fcis-10203	110	61	unifiedqa	unifiedqa	ADJ
fcis-10203	110	62	-	-	PUNCT
fcis-10203	110	63	v2	v2	NOUN
fcis-10203	110	64	spqaprompt	spqaprompt	NOUN
fcis-10203	110	65	unifiedqa	unifiedqa	ADJ
fcis-10203	110	66	-	-	PUNCT
fcis-10203	110	67	v2	v2	NOUN
fcis-10203	110	68	spqaprompt	spqaprompt	NOUN
fcis-10203	110	69	unifiedqa	unifiedqa	ADJ
fcis-10203	110	70	-	-	PUNCT
fcis-10203	110	71	v2	v2	NOUN
fcis-10203	110	72	spqaprompt	spqaprompt	NOUN
fcis-10203	110	73	unifiedqa	unifiedqa	ADJ
fcis-10203	110	74	-	-	PUNCT
fcis-10203	110	75	v2	v2	NOUN
fcis-10203	110	76	spqaprompt	spqaprompt	NOUN
fcis-10203	110	77	small	small	ADJ
fcis-10203	110	78	0.750	0.750	NUM
fcis-10203	110	79	0.650	0.650	NUM
fcis-10203	110	80	0.550	0.550	NUM
fcis-10203	110	81	0.500	0.500	NUM
fcis-10203	110	82	0.700	0.700	NUM
fcis-10203	110	83	0.700	0.700	NUM
fcis-10203	110	84	0.500	0.500	NUM
fcis-10203	110	85	0.400	0.400	NUM
fcis-10203	110	86	0.550	0.550	NUM
fcis-10203	110	87	0.550	0.550	NUM
fcis-10203	110	88	base	base	NOUN
fcis-10203	110	89	0.550	0.550	NUM
fcis-10203	110	90	0.550	0.550	NUM
fcis-10203	110	91	0.150	0.150	NUM
fcis-10203	110	92	0.300	0.300	NUM
fcis-10203	110	93	0.700	0.700	NUM
fcis-10203	110	94	0.700	0.700	NUM
fcis-10203	110	95	0.500	0.500	NUM
fcis-10203	110	96	0.400	0.400	NUM
fcis-10203	110	97	0.300	0.300	NUM
fcis-10203	110	98	0.500	0.500	NUM
fcis-10203	110	99	large	large	ADJ
fcis-10203	110	100	0.550	0.550	NUM
fcis-10203	110	101	0.550	0.550	NUM
fcis-10203	110	102	0.750	0.750	NUM
fcis-10203	110	103	0.600	0.600	NUM
fcis-10203	110	104	0.700	0.700	NUM
fcis-10203	110	105	0.650	0.650	NUM
fcis-10203	110	106	0.450	0.450	NUM
fcis-10203	110	107	0.450	0.450	NUM
fcis-10203	110	108	0.300	0.300	NUM
fcis-10203	110	109	0.650	0.650	NUM
fcis-10203	110	110	table	table	NOUN
fcis-10203	110	111	5	5	NUM
fcis-10203	110	112	.	.	PUNCT
fcis-10203	111	1	aggregate	aggregate	ADJ
fcis-10203	111	2	scores	score	NOUN
fcis-10203	111	3	that	that	PRON
fcis-10203	111	4	contrast	contrast	VERB
fcis-10203	111	5	the	the	DET
fcis-10203	111	6	two	two	NUM
fcis-10203	111	7	models	model	NOUN
fcis-10203	111	8	with	with	ADP
fcis-10203	111	9	prompt	prompt	ADJ
fcis-10203	111	10	-	-	PUNCT
fcis-10203	111	11	tuning	tune	VERB
fcis-10203	111	12	average	average	NOUN
fcis-10203	111	13	aggregated	aggregate	VERB
fcis-10203	111	14	unifiedqa	unifiedqa	ADV
fcis-10203	111	15	-	-	PUNCT
fcis-10203	111	16	v2	v2	NOUN
fcis-10203	111	17	spqa	spqa	NOUN
fcis-10203	111	18	sp	sp	NOUN
fcis-10203	111	19	-	-	PUNCT
fcis-10203	111	20	uni2	uni2	NOUN
fcis-10203	111	21	sp	sp	ADP
fcis-10203	111	22	uni2	uni2	PROPN
fcis-10203	111	23	?	?	PUNCT
fcis-10203	112	1	small	small	ADJ
fcis-10203	112	2	0.610	0.610	NUM
fcis-10203	112	3	0.560	0.560	NUM
fcis-10203	112	4	-0.050	-0.050	NUM
fcis-10203	112	5	33	33	NUM
fcis-10203	112	6	base	base	NOUN
fcis-10203	112	7	0.440	0.440	NUM
fcis-10203	112	8	0.490	0.490	NUM
fcis-10203	112	9	0.050	0.050	NUM
fcis-10203	112	10	83	83	NUM
fcis-10203	112	11	large	large	ADJ
fcis-10203	112	12	0.550	0.550	NUM
fcis-10203	112	13	0.580	0.580	NUM
fcis-10203	112	14	0.030	0.030	NUM
fcis-10203	112	15	67	67	NUM
fcis-10203	112	16	4.3	4.3	NUM
fcis-10203	112	17	.	.	PUNCT
fcis-10203	112	18	results	result	NOUN
fcis-10203	112	19	summarize	summarize	VERB
fcis-10203	112	20	the	the	DET
fcis-10203	112	21	tpu	tpu	PROPN
fcis-10203	112	22	(	(	PUNCT
fcis-10203	112	23	v2.8	v2.8	NOUN
fcis-10203	112	24	)	)	PUNCT
fcis-10203	112	25	fine	fine	ADV
fcis-10203	112	26	-	-	PUNCT
fcis-10203	112	27	tuning	tuning	NOUN
fcis-10203	112	28	results	result	NOUN
fcis-10203	112	29	from	from	ADP
fcis-10203	112	30	table	table	NOUN
fcis-10203	112	31	2	2	NUM
fcis-10203	112	32	and	and	CCONJ
fcis-10203	112	33	table	table	NOUN
fcis-10203	112	34	3	3	NUM
fcis-10203	112	35	.	.	PUNCT
fcis-10203	113	1	in	in	ADP
fcis-10203	113	2	all	all	DET
fcis-10203	113	3	experiments	experiment	NOUN
fcis-10203	113	4	,	,	PUNCT
fcis-10203	113	5	spqa	spqa	PROPN
fcis-10203	113	6	causes	cause	VERB
fcis-10203	113	7	2.23	2.23	NUM
fcis-10203	113	8	%	%	NOUN
fcis-10203	113	9	performance	performance	NOUN
fcis-10203	113	10	improvements	improvement	NOUN
fcis-10203	113	11	over	over	ADP
fcis-10203	113	12	unifiedqa	unifiedqa	NOUN
fcis-10203	113	13	-	-	PUNCT
fcis-10203	113	14	v2	v2	NOUN
fcis-10203	113	15	,	,	PUNCT
fcis-10203	113	16	on	on	ADP
fcis-10203	113	17	average	average	ADJ
fcis-10203	113	18	(	(	PUNCT
fcis-10203	113	19	‘	'	PUNCT
fcis-10203	113	20	sp	sp	ADP
fcis-10203	113	21	–	–	PUNCT
fcis-10203	113	22	uni2	uni2	NOUN
fcis-10203	113	23	’	'	PUNCT
fcis-10203	113	24	)	)	PUNCT
fcis-10203	113	25	.	.	PUNCT
fcis-10203	114	1	the	the	DET
fcis-10203	114	2	highest	high	ADJ
fcis-10203	114	3	gains	gain	NOUN
fcis-10203	114	4	appear	appear	VERB
fcis-10203	114	5	on	on	ADP
fcis-10203	114	6	mid	mid	ADJ
fcis-10203	114	7	-	-	ADJ
fcis-10203	114	8	sized	sized	ADJ
fcis-10203	114	9	‘	'	PUNCT
fcis-10203	114	10	base	base	NOUN
fcis-10203	114	11	’	'	PUNCT
fcis-10203	114	12	models	model	NOUN
fcis-10203	114	13	(	(	PUNCT
fcis-10203	114	14	9.01	9.01	NUM
fcis-10203	114	15	%	%	NOUN
fcis-10203	114	16	for	for	ADP
fcis-10203	114	17	overall	overall	ADJ
fcis-10203	114	18	,	,	PUNCT
fcis-10203	114	19	6.22	6.22	NUM
fcis-10203	114	20	%	%	NOUN
fcis-10203	114	21	for	for	ADP
fcis-10203	114	22	in	in	ADP
fcis-10203	114	23	-	-	PUNCT
fcis-10203	114	24	domain	domain	NOUN
fcis-10203	114	25	and	and	CCONJ
fcis-10203	114	26	14.4	14.4	NUM
fcis-10203	114	27	%	%	NOUN
fcis-10203	114	28	for	for	ADP
fcis-10203	114	29	out	out	ADV
fcis-10203	114	30	-	-	PUNCT
fcis-10203	114	31	of	of	ADP
fcis-10203	114	32	-	-	PUNCT
fcis-10203	114	33	domain	domain	NOUN
fcis-10203	114	34	)	)	PUNCT
fcis-10203	114	35	.	.	PUNCT
fcis-10203	115	1	on	on	ADP
fcis-10203	115	2	the	the	DET
fcis-10203	115	3	contrary	contrary	NOUN
fcis-10203	115	4	,	,	PUNCT
fcis-10203	115	5	the	the	DET
fcis-10203	115	6	lowest	low	ADJ
fcis-10203	115	7	gains	gain	NOUN
fcis-10203	115	8	appear	appear	VERB
fcis-10203	115	9	on	on	ADP
fcis-10203	115	10	the	the	DET
fcis-10203	115	11	extreme	extreme	ADJ
fcis-10203	115	12	sizes	size	NOUN
fcis-10203	115	13	(	(	PUNCT
fcis-10203	115	14	‘	'	PUNCT
fcis-10203	115	15	small	small	ADJ
fcis-10203	115	16	’	'	PUNCT
fcis-10203	115	17	and	and	CCONJ
fcis-10203	115	18	‘	'	PUNCT
fcis-10203	115	19	3b	3b	NUM
fcis-10203	115	20	’	'	PUNCT
fcis-10203	115	21	)	)	PUNCT
fcis-10203	115	22	.	.	PUNCT
fcis-10203	116	1	similar	similar	ADJ
fcis-10203	116	2	tendency	tendency	NOUN
fcis-10203	116	3	shows	show	VERB
fcis-10203	116	4	with	with	ADP
fcis-10203	116	5	‘	'	PUNCT
fcis-10203	116	6	sp	sp	ADP
fcis-10203	116	7	uni2	uni2	NOUN
fcis-10203	116	8	?	?	PUNCT
fcis-10203	116	9	’	'	PUNCT
fcis-10203	117	1	metric	metric	ADJ
fcis-10203	117	2	(	(	PUNCT
fcis-10203	117	3	percentage	percentage	NOUN
fcis-10203	117	4	that	that	PRON
fcis-10203	117	5	spqa	spqa	NOUN
fcis-10203	117	6	outperforms	outperform	VERB
fcis-10203	117	7	unifiedqa	unifiedqa	NOUN
fcis-10203	117	8	-	-	PUNCT
fcis-10203	117	9	v2	v2	NOUN
fcis-10203	117	10	)	)	PUNCT
fcis-10203	117	11	.	.	PUNCT
fcis-10203	118	1	on	on	ADP
fcis-10203	118	2	this	this	DET
fcis-10203	118	3	metric	metric	NOUN
fcis-10203	118	4	,	,	PUNCT
fcis-10203	118	5	all	all	DET
fcis-10203	118	6	the	the	DET
fcis-10203	118	7	numbers	number	NOUN
fcis-10203	118	8	are	be	AUX
fcis-10203	118	9	only	only	ADV
fcis-10203	118	10	above	above	ADP
fcis-10203	118	11	10	10	NUM
fcis-10203	118	12	%	%	NOUN
fcis-10203	118	13	,	,	PUNCT
fcis-10203	118	14	which	which	PRON
fcis-10203	118	15	demonstrates	demonstrate	VERB
fcis-10203	118	16	that	that	SCONJ
fcis-10203	118	17	spqa	spqa	NOUN
fcis-10203	118	18	models	model	NOUN
fcis-10203	118	19	generally	generally	ADV
fcis-10203	118	20	do	do	AUX
fcis-10203	118	21	n’t	not	PART
fcis-10203	118	22	causes	cause	VERB
fcis-10203	118	23	better	well	ADJ
fcis-10203	118	24	performance	performance	NOUN
fcis-10203	118	25	on	on	ADP
fcis-10203	118	26	all	all	PRON
fcis-10203	118	27	yes	yes	NOUN
fcis-10203	118	28	/	/	SYM
fcis-10203	119	1	no	no	DET
fcis-10203	119	2	qa	qa	PROPN
fcis-10203	119	3	datasets	dataset	NOUN
fcis-10203	119	4	.	.	PUNCT
fcis-10203	120	1	however	however	ADV
fcis-10203	120	2	,	,	PUNCT
fcis-10203	120	3	on	on	ADP
fcis-10203	120	4	spqtest(unseen	spqtest(unseen	PROPN
fcis-10203	120	5	)	)	PUNCT
fcis-10203	120	6	,	,	PUNCT
fcis-10203	120	7	particularly	particularly	ADV
fcis-10203	120	8	,	,	PUNCT
fcis-10203	120	9	the	the	DET
fcis-10203	120	10	numbers	number	NOUN
fcis-10203	120	11	are	be	AUX
fcis-10203	120	12	always	always	ADV
fcis-10203	120	13	100	100	NUM
fcis-10203	120	14	%	%	NOUN
fcis-10203	120	15	,	,	PUNCT
fcis-10203	120	16	which	which	PRON
fcis-10203	120	17	demonstrates	demonstrate	VERB
fcis-10203	120	18	that	that	SCONJ
fcis-10203	120	19	spqa	spqa	NOUN
fcis-10203	120	20	models	model	NOUN
fcis-10203	120	21	causes	cause	VERB
fcis-10203	120	22	better	well	ADJ
fcis-10203	120	23	performance	performance	NOUN
fcis-10203	120	24	on	on	ADP
fcis-10203	120	25	all	all	DET
fcis-10203	120	26	situation	situation	NOUN
fcis-10203	120	27	puzzle	puzzle	NOUN
fcis-10203	120	28	qa	qa	PROPN
fcis-10203	120	29	datasets	dataset	NOUN
fcis-10203	120	30	.	.	PUNCT
fcis-10203	121	1	in	in	ADP
fcis-10203	121	2	addition	addition	NOUN
fcis-10203	121	3	,	,	PUNCT
fcis-10203	121	4	spqa	spqa	PROPN
fcis-10203	121	5	of	of	ADP
fcis-10203	121	6	size‘base	size‘base	PROPN
fcis-10203	121	7	’	'	PUNCT
fcis-10203	121	8	outperforms	outperform	VERB
fcis-10203	121	9	unifiedqa	unifiedqa	ADV
fcis-10203	121	10	-	-	PUNCT
fcis-10203	121	11	v2	v2	NOUN
fcis-10203	121	12	of	of	ADP
fcis-10203	121	13	the	the	DET
fcis-10203	121	14	same	same	ADJ
fcis-10203	121	15	size	size	NOUN
fcis-10203	121	16	on	on	ADP
fcis-10203	121	17	both	both	DET
fcis-10203	121	18	100	100	NUM
fcis-10203	121	19	%	%	NOUN
fcis-10203	121	20	of	of	ADP
fcis-10203	121	21	the	the	DET
fcis-10203	121	22	datasets	dataset	NOUN
fcis-10203	121	23	,	,	PUNCT
fcis-10203	121	24	for	for	ADP
fcis-10203	121	25	in	in	ADP
fcis-10203	121	26	-	-	PUNCT
fcis-10203	121	27	domain	domain	NOUN
fcis-10203	121	28	and	and	CCONJ
fcis-10203	121	29	outdomain	outdomain	NOUN
fcis-10203	121	30	datasets	dataset	NOUN
fcis-10203	121	31	.	.	PUNCT
fcis-10203	122	1	according	accord	VERB
fcis-10203	122	2	to	to	ADP
fcis-10203	122	3	the	the	DET
fcis-10203	122	4	results	result	NOUN
fcis-10203	122	5	of	of	ADP
fcis-10203	122	6	fine	fine	ADV
fcis-10203	122	7	-	-	PUNCT
fcis-10203	122	8	tuning	tune	VERB
fcis-10203	122	9	part	part	NOUN
fcis-10203	122	10	,	,	PUNCT
fcis-10203	122	11	unifiedqav2	unifiedqav2	PROPN
fcis-10203	122	12	always	always	ADV
fcis-10203	122	13	keeps	keep	VERB
fcis-10203	122	14	the	the	DET
fcis-10203	122	15	performance	performance	NOUN
fcis-10203	122	16	well	well	ADV
fcis-10203	122	17	,	,	PUNCT
fcis-10203	122	18	especially	especially	ADV
fcis-10203	122	19	on	on	ADP
fcis-10203	122	20	normal	normal	ADJ
fcis-10203	123	1	yes	yes	INTJ
fcis-10203	123	2	/	/	SYM
fcis-10203	123	3	no	no	PRON
fcis-10203	123	4	questions	question	NOUN
fcis-10203	123	5	for	for	ADP
fcis-10203	123	6	small	small	ADJ
fcis-10203	123	7	and	and	CCONJ
fcis-10203	123	8	3b	3b	NUM
fcis-10203	123	9	.	.	PUNCT
fcis-10203	124	1	on	on	ADP
fcis-10203	124	2	the	the	DET
fcis-10203	124	3	other	other	ADJ
fcis-10203	124	4	side	side	NOUN
fcis-10203	124	5	,	,	PUNCT
fcis-10203	124	6	the	the	DET
fcis-10203	124	7	spqa	spqa	NOUN
fcis-10203	124	8	always	always	ADV
fcis-10203	124	9	keeps	keep	VERB
fcis-10203	124	10	better	well	ADJ
fcis-10203	124	11	performance	performance	NOUN
fcis-10203	124	12	than	than	ADP
fcis-10203	124	13	unifiedqav2	unifiedqav2	NOUN
fcis-10203	124	14	,	,	PUNCT
fcis-10203	124	15	especially	especially	ADV
fcis-10203	124	16	on	on	ADP
fcis-10203	124	17	situation	situation	NOUN
fcis-10203	124	18	puzzle	puzzle	NOUN
fcis-10203	124	19	questions	question	NOUN
fcis-10203	124	20	from	from	ADP
fcis-10203	124	21	small	small	ADJ
fcis-10203	124	22	to	to	ADP
fcis-10203	124	23	3b	3b	NUM
fcis-10203	124	24	.	.	PUNCT
fcis-10203	125	1	when	when	SCONJ
fcis-10203	125	2	the	the	DET
fcis-10203	125	3	model	model	NOUN
fcis-10203	125	4	scales	scale	NOUN
fcis-10203	125	5	are	be	AUX
fcis-10203	125	6	‘	'	PUNCT
fcis-10203	125	7	base	base	NOUN
fcis-10203	125	8	’	'	PUNCT
fcis-10203	125	9	and	and	CCONJ
fcis-10203	125	10	‘	'	PUNCT
fcis-10203	125	11	large	large	ADJ
fcis-10203	125	12	’	'	PUNCT
fcis-10203	125	13	,	,	PUNCT
fcis-10203	125	14	spqa	spqa	PROPN
fcis-10203	125	15	is	be	AUX
fcis-10203	125	16	faster	fast	ADJ
fcis-10203	125	17	than	than	ADP
fcis-10203	125	18	unifiedqa	unifiedqa	ADV
fcis-10203	125	19	-	-	PUNCT
fcis-10203	125	20	v2	v2	NOUN
fcis-10203	125	21	to	to	PART
fcis-10203	125	22	get	get	VERB
fcis-10203	125	23	the	the	DET
fcis-10203	125	24	receptable	receptable	ADJ
fcis-10203	125	25	results	result	NOUN
fcis-10203	125	26	.	.	PUNCT
fcis-10203	126	1	when	when	SCONJ
fcis-10203	126	2	the	the	DET
fcis-10203	126	3	model	model	NOUN
fcis-10203	126	4	scale	scale	NOUN
fcis-10203	126	5	reaches	reach	VERB
fcis-10203	126	6	to	to	ADP
fcis-10203	126	7	3b	3b	NUM
fcis-10203	126	8	,	,	PUNCT
fcis-10203	126	9	unifiedqa	unifiedqa	ADJ
fcis-10203	126	10	-	-	PUNCT
fcis-10203	126	11	v2	v2	NOUN
fcis-10203	126	12	begin	begin	NOUN
fcis-10203	126	13	to	to	PART
fcis-10203	126	14	surpassed	surpass	VERB
fcis-10203	126	15	spqa	spqa	PROPN
fcis-10203	126	16	on	on	ADP
fcis-10203	126	17	all	all	DET
fcis-10203	126	18	the	the	DET
fcis-10203	126	19	yes	yes	INTJ
fcis-10203	126	20	/	/	SYM
fcis-10203	126	21	no	no	DET
fcis-10203	126	22	questions	question	NOUN
fcis-10203	126	23	,	,	PUNCT
fcis-10203	126	24	which	which	PRON
fcis-10203	126	25	means	mean	VERB
fcis-10203	126	26	the	the	DET
fcis-10203	126	27	situation	situation	NOUN
fcis-10203	126	28	puzzle	puzzle	NOUN
fcis-10203	126	29	training	training	NOUN
fcis-10203	126	30	dataset	dataset	NOUN
fcis-10203	126	31	may	may	AUX
fcis-10203	126	32	affect	affect	VERB
fcis-10203	126	33	the	the	DET
fcis-10203	126	34	system	system	NOUN
fcis-10203	126	35	to	to	PART
fcis-10203	126	36	judge	judge	VERB
fcis-10203	126	37	the	the	DET
fcis-10203	126	38	normal	normal	ADJ
fcis-10203	126	39	questions	question	NOUN
fcis-10203	126	40	.	.	PUNCT
fcis-10203	127	1	summarizing	summarize	VERB
fcis-10203	127	2	the	the	DET
fcis-10203	127	3	gpu	gpu	NOUN
fcis-10203	127	4	prompt	prompt	ADJ
fcis-10203	127	5	-	-	PUNCT
fcis-10203	127	6	tuning	tuning	NOUN
fcis-10203	127	7	results	result	NOUN
fcis-10203	127	8	from	from	ADP
fcis-10203	127	9	table	table	NOUN
fcis-10203	127	10	4	4	NUM
fcis-10203	127	11	and	and	CCONJ
fcis-10203	127	12	table	table	NOUN
fcis-10203	127	13	5	5	NUM
fcis-10203	127	14	.	.	PUNCT
fcis-10203	128	1	in	in	ADP
fcis-10203	128	2	all	all	DET
fcis-10203	128	3	experiments	experiment	NOUN
fcis-10203	128	4	,	,	PUNCT
fcis-10203	128	5	spqa	spqa	NOUN
fcis-10203	128	6	-	-	PUNCT
fcis-10203	128	7	prompt	prompt	NOUN
fcis-10203	128	8	causes	cause	VERB
fcis-10203	128	9	1.88	1.88	NUM
fcis-10203	128	10	%	%	NOUN
fcis-10203	128	11	performance	performance	NOUN
fcis-10203	128	12	improvements	improvement	NOUN
fcis-10203	128	13	over	over	ADP
fcis-10203	128	14	unifiedqa	unifiedqa	NOUN
fcis-10203	128	15	-	-	PUNCT
fcis-10203	128	16	v2	v2	NOUN
fcis-10203	128	17	-	-	PUNCT
fcis-10203	128	18	prompt	prompt	NOUN
fcis-10203	128	19	,	,	PUNCT
fcis-10203	128	20	on	on	ADP
fcis-10203	128	21	average	average	ADJ
fcis-10203	128	22	(	(	PUNCT
fcis-10203	128	23	‘	'	PUNCT
fcis-10203	128	24	sp	sp	ADP
fcis-10203	128	25	–	–	PUNCT
fcis-10203	128	26	uni2	uni2	NOUN
fcis-10203	128	27	’	'	PUNCT
fcis-10203	128	28	)	)	PUNCT
fcis-10203	128	29	.	.	PUNCT
fcis-10203	129	1	the	the	DET
fcis-10203	129	2	highest	high	ADJ
fcis-10203	129	3	gains	gain	NOUN
fcis-10203	129	4	appear	appear	VERB
fcis-10203	129	5	on	on	ADP
fcis-10203	129	6	midsized	midsized	ADJ
fcis-10203	129	7	‘	'	PUNCT
fcis-10203	129	8	base	base	NOUN
fcis-10203	129	9	’	'	PUNCT
fcis-10203	129	10	models	model	NOUN
fcis-10203	129	11	(	(	PUNCT
fcis-10203	129	12	11.36	11.36	NUM
fcis-10203	129	13	%	%	NOUN
fcis-10203	129	14	for	for	ADP
fcis-10203	129	15	overall	overall	ADJ
fcis-10203	129	16	,	,	PUNCT
fcis-10203	129	17	4.17	4.17	NUM
fcis-10203	129	18	%	%	NOUN
fcis-10203	129	19	for	for	ADP
fcis-10203	129	20	in	in	ADP
fcis-10203	129	21	-	-	PUNCT
fcis-10203	129	22	domain	domain	NOUN
fcis-10203	129	23	and	and	CCONJ
fcis-10203	129	24	20.0	20.0	NUM
fcis-10203	129	25	%	%	NOUN
fcis-10203	129	26	for	for	ADP
fcis-10203	129	27	out	out	ADV
fcis-10203	129	28	-	-	PUNCT
fcis-10203	129	29	of	of	ADP
fcis-10203	129	30	-	-	PUNCT
fcis-10203	129	31	domain	domain	NOUN
fcis-10203	129	32	)	)	PUNCT
fcis-10203	129	33	.	.	PUNCT
fcis-10203	130	1	on	on	ADP
fcis-10203	130	2	the	the	DET
fcis-10203	130	3	contrary	contrary	NOUN
fcis-10203	130	4	,	,	PUNCT
fcis-10203	130	5	the	the	DET
fcis-10203	130	6	lowest	low	ADJ
fcis-10203	130	7	gains	gain	NOUN
fcis-10203	130	8	appear	appear	VERB
fcis-10203	130	9	on	on	ADP
fcis-10203	130	10	the	the	DET
fcis-10203	130	11	extreme	extreme	ADJ
fcis-10203	130	12	size	size	NOUN
fcis-10203	130	13	(	(	PUNCT
fcis-10203	130	14	‘	'	PUNCT
fcis-10203	130	15	small	small	ADJ
fcis-10203	130	16	’	'	PUNCT
fcis-10203	130	17	)	)	PUNCT
fcis-10203	130	18	.	.	PUNCT
fcis-10203	131	1	similar	similar	ADJ
fcis-10203	131	2	tendency	tendency	NOUN
fcis-10203	131	3	shows	show	VERB
fcis-10203	131	4	with	with	ADP
fcis-10203	131	5	‘	'	PUNCT
fcis-10203	131	6	sp	sp	ADP
fcis-10203	131	7	uni2	uni2	NOUN
fcis-10203	131	8	?	?	PUNCT
fcis-10203	131	9	’	'	PUNCT
fcis-10203	132	1	metric	metric	ADJ
fcis-10203	132	2	(	(	PUNCT
fcis-10203	132	3	percentage	percentage	NOUN
fcis-10203	132	4	that	that	PRON
fcis-10203	132	5	spqa	spqa	NOUN
fcis-10203	132	6	-	-	PUNCT
fcis-10203	132	7	prompt	prompt	NOUN
fcis-10203	132	8	outperforms	outperform	NOUN
fcis-10203	132	9	unifiedqa	unifiedqa	VERB
fcis-10203	132	10	66	66	NUM
fcis-10203	132	11	v2	v2	NOUN
fcis-10203	132	12	-	-	PUNCT
fcis-10203	132	13	prompt	prompt	NOUN
fcis-10203	132	14	)	)	PUNCT
fcis-10203	132	15	.	.	PUNCT
fcis-10203	133	1	on	on	ADP
fcis-10203	133	2	this	this	DET
fcis-10203	133	3	metric	metric	NOUN
fcis-10203	133	4	,	,	PUNCT
fcis-10203	133	5	all	all	DET
fcis-10203	133	6	the	the	DET
fcis-10203	133	7	numbers	number	NOUN
fcis-10203	133	8	are	be	AUX
fcis-10203	133	9	only	only	ADV
fcis-10203	133	10	above	above	ADP
fcis-10203	133	11	30	30	NUM
fcis-10203	133	12	%	%	NOUN
fcis-10203	133	13	,	,	PUNCT
fcis-10203	133	14	which	which	PRON
fcis-10203	133	15	demonstrates	demonstrate	VERB
fcis-10203	133	16	that	that	SCONJ
fcis-10203	133	17	spqa	spqa	NOUN
fcis-10203	133	18	models	model	NOUN
fcis-10203	133	19	generally	generally	ADV
fcis-10203	133	20	do	do	AUX
fcis-10203	133	21	n’t	not	PART
fcis-10203	133	22	causes	cause	VERB
fcis-10203	133	23	better	well	ADJ
fcis-10203	133	24	performance	performance	NOUN
fcis-10203	133	25	on	on	ADP
fcis-10203	133	26	all	all	PRON
fcis-10203	133	27	yes	yes	NOUN
fcis-10203	133	28	/	/	SYM
fcis-10203	134	1	no	no	DET
fcis-10203	134	2	qa	qa	PROPN
fcis-10203	134	3	datasets	dataset	NOUN
fcis-10203	134	4	.	.	PUNCT
fcis-10203	135	1	however	however	ADV
fcis-10203	135	2	,	,	PUNCT
fcis-10203	135	3	on	on	ADP
fcis-10203	135	4	spq	spq	NOUN
fcis-10203	135	5	-	-	PUNCT
fcis-10203	135	6	test(unseen	test(unseen	PROPN
fcis-10203	135	7	)	)	PUNCT
fcis-10203	135	8	,	,	PUNCT
fcis-10203	135	9	particularly	particularly	ADV
fcis-10203	135	10	,	,	PUNCT
fcis-10203	135	11	the	the	DET
fcis-10203	135	12	numbers	number	NOUN
fcis-10203	135	13	are	be	AUX
fcis-10203	135	14	always	always	ADV
fcis-10203	135	15	100	100	NUM
fcis-10203	135	16	%	%	NOUN
fcis-10203	135	17	,	,	PUNCT
fcis-10203	135	18	which	which	PRON
fcis-10203	135	19	demonstrates	demonstrate	VERB
fcis-10203	135	20	that	that	SCONJ
fcis-10203	135	21	spqa	spqa	NOUN
fcis-10203	135	22	models	model	NOUN
fcis-10203	135	23	causes	cause	VERB
fcis-10203	135	24	better	well	ADJ
fcis-10203	135	25	performance	performance	NOUN
fcis-10203	135	26	on	on	ADP
fcis-10203	135	27	all	all	DET
fcis-10203	135	28	situation	situation	NOUN
fcis-10203	135	29	puzzle	puzzle	NOUN
fcis-10203	135	30	qa	qa	PROPN
fcis-10203	135	31	datasets	dataset	NOUN
fcis-10203	135	32	.	.	PUNCT
fcis-10203	136	1	in	in	ADP
fcis-10203	136	2	addition	addition	NOUN
fcis-10203	136	3	,	,	PUNCT
fcis-10203	136	4	spqa	spqa	PROPN
fcis-10203	136	5	of	of	ADP
fcis-10203	136	6	size‘base	size‘base	PROPN
fcis-10203	136	7	’	'	PUNCT
fcis-10203	136	8	outperforms	outperform	VERB
fcis-10203	136	9	unifiedqa	unifiedqa	ADJ
fcis-10203	136	10	-	-	PUNCT
fcis-10203	136	11	v2prompt	v2prompt	NOUN
fcis-10203	136	12	of	of	ADP
fcis-10203	136	13	the	the	DET
fcis-10203	136	14	same	same	ADJ
fcis-10203	136	15	size	size	NOUN
fcis-10203	136	16	on	on	ADP
fcis-10203	136	17	66.7	66.7	NUM
fcis-10203	136	18	%	%	NOUN
fcis-10203	136	19	and	and	CCONJ
fcis-10203	136	20	100	100	NUM
fcis-10203	136	21	%	%	NOUN
fcis-10203	136	22	of	of	ADP
fcis-10203	136	23	the	the	DET
fcis-10203	136	24	datasets	dataset	NOUN
fcis-10203	136	25	,	,	PUNCT
fcis-10203	136	26	for	for	ADP
fcis-10203	136	27	in	in	ADP
fcis-10203	136	28	-	-	PUNCT
fcis-10203	136	29	domain	domain	NOUN
fcis-10203	136	30	and	and	CCONJ
fcis-10203	136	31	out	out	ADJ
fcis-10203	136	32	-	-	PUNCT
fcis-10203	136	33	domain	domain	NOUN
fcis-10203	136	34	datasets	dataset	NOUN
fcis-10203	136	35	,	,	PUNCT
fcis-10203	136	36	respectively	respectively	ADV
fcis-10203	136	37	.	.	PUNCT
fcis-10203	137	1	according	accord	VERB
fcis-10203	137	2	to	to	ADP
fcis-10203	137	3	the	the	DET
fcis-10203	137	4	results	result	NOUN
fcis-10203	137	5	of	of	ADP
fcis-10203	137	6	prompt	prompt	NOUN
fcis-10203	137	7	-	-	PUNCT
fcis-10203	137	8	tuning	tune	VERB
fcis-10203	137	9	part	part	NOUN
fcis-10203	137	10	,	,	PUNCT
fcis-10203	137	11	all	all	DET
fcis-10203	137	12	datasets	dataset	NOUN
fcis-10203	137	13	trained	train	VERB
fcis-10203	137	14	under	under	ADP
fcis-10203	137	15	the	the	DET
fcis-10203	137	16	same	same	ADJ
fcis-10203	137	17	data	data	NOUN
fcis-10203	137	18	scale	scale	NOUN
fcis-10203	137	19	.	.	PUNCT
fcis-10203	138	1	unifiedqa	unifiedqa	NOUN
fcis-10203	138	2	-	-	PUNCT
fcis-10203	138	3	v2	v2	PROPN
fcis-10203	138	4	and	and	CCONJ
fcis-10203	138	5	spqa	spqa	NOUN
fcis-10203	138	6	keep	keep	VERB
fcis-10203	138	7	the	the	DET
fcis-10203	138	8	similar	similar	ADJ
fcis-10203	138	9	performance	performance	NOUN
fcis-10203	138	10	for	for	ADP
fcis-10203	138	11	‘	'	PUNCT
fcis-10203	138	12	small	small	ADJ
fcis-10203	138	13	’	'	PUNCT
fcis-10203	138	14	and	and	CCONJ
fcis-10203	138	15	‘	'	PUNCT
fcis-10203	138	16	base	base	NOUN
fcis-10203	138	17	’	'	PUNCT
fcis-10203	138	18	.	.	PUNCT
fcis-10203	139	1	for	for	ADP
fcis-10203	139	2	‘	'	PUNCT
fcis-10203	139	3	large	large	ADJ
fcis-10203	139	4	’	'	PUNCT
fcis-10203	139	5	model	model	NOUN
fcis-10203	139	6	,	,	PUNCT
fcis-10203	139	7	the	the	DET
fcis-10203	139	8	situation	situation	NOUN
fcis-10203	139	9	puzzle	puzzle	NOUN
fcis-10203	139	10	dataset	dataset	NOUN
fcis-10203	139	11	affects	affect	VERB
fcis-10203	139	12	more	more	ADV
fcis-10203	139	13	significantly	significantly	ADV
fcis-10203	139	14	,	,	PUNCT
fcis-10203	139	15	make	make	VERB
fcis-10203	139	16	the	the	DET
fcis-10203	139	17	performance	performance	NOUN
fcis-10203	139	18	of	of	ADP
fcis-10203	139	19	spq	spq	NOUN
fcis-10203	139	20	test	test	NOUN
fcis-10203	139	21	is	be	AUX
fcis-10203	139	22	better	well	ADJ
fcis-10203	139	23	and	and	CCONJ
fcis-10203	139	24	make	make	VERB
fcis-10203	139	25	the	the	DET
fcis-10203	139	26	performance	performance	NOUN
fcis-10203	139	27	of	of	ADP
fcis-10203	139	28	other	other	ADJ
fcis-10203	139	29	normal	normal	ADJ
fcis-10203	139	30	yes	yes	INTJ
fcis-10203	139	31	/	/	SYM
fcis-10203	139	32	no	no	DET
fcis-10203	139	33	datasets	dataset	NOUN
fcis-10203	139	34	worse	bad	ADJ
fcis-10203	139	35	.	.	PUNCT
fcis-10203	140	1	4.4	4.4	NUM
fcis-10203	140	2	.	.	PUNCT
fcis-10203	140	3	spqa	spqa	PROPN
fcis-10203	140	4	vs	vs	ADP
fcis-10203	140	5	unifiedqa	unifiedqa	NOUN
fcis-10203	140	6	vs	vs	ADP
fcis-10203	140	7	chatgpt	chatgpt	NOUN
fcis-10203	140	8	(	(	PUNCT
fcis-10203	140	9	gpt-3.5	gpt-3.5	NOUN
fcis-10203	140	10	)	)	PUNCT
fcis-10203	140	11	according	accord	VERB
fcis-10203	140	12	to	to	ADP
fcis-10203	140	13	the	the	DET
fcis-10203	140	14	table	table	NOUN
fcis-10203	140	15	6	6	NUM
fcis-10203	140	16	,	,	PUNCT
fcis-10203	140	17	i	i	PRON
fcis-10203	140	18	take	take	VERB
fcis-10203	140	19	some	some	DET
fcis-10203	140	20	examples	example	NOUN
fcis-10203	140	21	as	as	ADP
fcis-10203	140	22	the	the	DET
fcis-10203	140	23	reference	reference	NOUN
fcis-10203	140	24	.	.	PUNCT
fcis-10203	141	1	when	when	SCONJ
fcis-10203	141	2	the	the	DET
fcis-10203	141	3	contents	content	NOUN
fcis-10203	141	4	of	of	ADP
fcis-10203	141	5	a	a	DET
fcis-10203	141	6	situation	situation	NOUN
fcis-10203	141	7	puzzle	puzzle	NOUN
fcis-10203	141	8	are	be	AUX
fcis-10203	141	9	about	about	ADP
fcis-10203	141	10	the	the	DET
fcis-10203	141	11	crime	crime	NOUN
fcis-10203	141	12	or	or	CCONJ
fcis-10203	141	13	the	the	DET
fcis-10203	141	14	negative	negative	ADJ
fcis-10203	141	15	information	information	NOUN
fcis-10203	141	16	,	,	PUNCT
fcis-10203	141	17	the	the	DET
fcis-10203	141	18	chatgpt	chatgpt	NOUN
fcis-10203	141	19	will	will	AUX
fcis-10203	141	20	reject	reject	VERB
fcis-10203	141	21	the	the	DET
fcis-10203	141	22	requests	request	NOUN
fcis-10203	141	23	because	because	SCONJ
fcis-10203	141	24	of	of	ADP
fcis-10203	141	25	the	the	DET
fcis-10203	141	26	legal	legal	ADJ
fcis-10203	141	27	reasons	reason	NOUN
fcis-10203	141	28	even	even	ADV
fcis-10203	141	29	though	though	SCONJ
fcis-10203	141	30	this	this	DET
fcis-10203	141	31	topic	topic	NOUN
fcis-10203	141	32	is	be	AUX
fcis-10203	141	33	just	just	ADV
fcis-10203	141	34	a	a	DET
fcis-10203	141	35	story	story	NOUN
fcis-10203	141	36	.	.	PUNCT
fcis-10203	142	1	when	when	SCONJ
fcis-10203	142	2	the	the	DET
fcis-10203	142	3	contents	content	NOUN
fcis-10203	142	4	of	of	ADP
fcis-10203	142	5	a	a	DET
fcis-10203	142	6	situation	situation	NOUN
fcis-10203	142	7	puzzle	puzzle	NOUN
fcis-10203	142	8	are	be	AUX
fcis-10203	142	9	about	about	ADP
fcis-10203	142	10	the	the	DET
fcis-10203	142	11	other	other	ADJ
fcis-10203	142	12	aspects	aspect	NOUN
fcis-10203	142	13	,	,	PUNCT
fcis-10203	142	14	the	the	DET
fcis-10203	142	15	chatgpt	chatgpt	NOUN
fcis-10203	142	16	may	may	AUX
fcis-10203	142	17	sometimes	sometimes	ADV
fcis-10203	142	18	give	give	VERB
fcis-10203	142	19	the	the	DET
fcis-10203	142	20	answers	answer	NOUN
fcis-10203	142	21	such	such	ADJ
fcis-10203	142	22	as	as	ADP
fcis-10203	142	23	“	"	PUNCT
fcis-10203	142	24	the	the	DET
fcis-10203	142	25	information	information	NOUN
fcis-10203	142	26	given	give	VERB
fcis-10203	142	27	is	be	AUX
fcis-10203	142	28	not	not	PART
fcis-10203	142	29	clear	clear	ADJ
fcis-10203	142	30	”	"	PUNCT
fcis-10203	142	31	,	,	PUNCT
fcis-10203	142	32	“	"	PUNCT
fcis-10203	142	33	ca	can	AUX
fcis-10203	142	34	n’t	not	PART
fcis-10203	142	35	answer	answer	VERB
fcis-10203	142	36	and	and	CCONJ
fcis-10203	142	37	need	need	VERB
fcis-10203	142	38	more	more	ADJ
fcis-10203	142	39	information	information	NOUN
fcis-10203	142	40	”	"	PUNCT
fcis-10203	142	41	or	or	CCONJ
fcis-10203	142	42	“	"	PUNCT
fcis-10203	142	43	not	not	PART
fcis-10203	142	44	mentioned	mention	VERB
fcis-10203	142	45	in	in	ADP
fcis-10203	142	46	the	the	DET
fcis-10203	142	47	story	story	NOUN
fcis-10203	142	48	”	"	PUNCT
fcis-10203	142	49	,	,	PUNCT
fcis-10203	142	50	means	mean	VERB
fcis-10203	142	51	that	that	SCONJ
fcis-10203	142	52	chatgpt	chatgpt	NOUN
fcis-10203	142	53	need	need	VERB
fcis-10203	142	54	more	more	ADJ
fcis-10203	142	55	information	information	NOUN
fcis-10203	142	56	about	about	ADP
fcis-10203	142	57	the	the	DET
fcis-10203	142	58	stories	story	NOUN
fcis-10203	142	59	clearly	clearly	ADV
fcis-10203	142	60	mentioned	mention	VERB
fcis-10203	142	61	in	in	ADP
fcis-10203	142	62	these	these	DET
fcis-10203	142	63	questions	question	NOUN
fcis-10203	142	64	.	.	PUNCT
fcis-10203	143	1	in	in	ADP
fcis-10203	143	2	these	these	DET
fcis-10203	143	3	situation	situation	NOUN
fcis-10203	143	4	-	-	PUNCT
fcis-10203	143	5	puzzle	puzzle	NOUN
fcis-10203	143	6	questions	question	NOUN
fcis-10203	143	7	,	,	PUNCT
fcis-10203	143	8	chatgpt	chatgpt	NOUN
fcis-10203	143	9	has	have	VERB
fcis-10203	143	10	a	a	DET
fcis-10203	143	11	serious	serious	ADJ
fcis-10203	143	12	attitude	attitude	NOUN
fcis-10203	143	13	and	and	CCONJ
fcis-10203	143	14	will	will	AUX
fcis-10203	143	15	refuse	refuse	VERB
fcis-10203	143	16	to	to	PART
fcis-10203	143	17	answer	answer	VERB
fcis-10203	143	18	questions	question	NOUN
fcis-10203	143	19	without	without	ADP
fcis-10203	143	20	clear	clear	ADJ
fcis-10203	143	21	answers	answer	NOUN
fcis-10203	143	22	,	,	PUNCT
fcis-10203	143	23	like	like	ADP
fcis-10203	143	24	serious	serious	ADJ
fcis-10203	143	25	humans	human	NOUN
fcis-10203	143	26	.	.	PUNCT
fcis-10203	144	1	however	however	ADV
fcis-10203	144	2	,	,	PUNCT
fcis-10203	144	3	for	for	ADP
fcis-10203	144	4	this	this	DET
fcis-10203	144	5	point	point	NOUN
fcis-10203	144	6	,	,	PUNCT
fcis-10203	144	7	the	the	DET
fcis-10203	144	8	rules	rule	NOUN
fcis-10203	144	9	need	need	VERB
fcis-10203	144	10	the	the	DET
fcis-10203	144	11	model	model	NOUN
fcis-10203	144	12	to	to	PART
fcis-10203	144	13	answer	answer	VERB
fcis-10203	144	14	only	only	ADV
fcis-10203	144	15	by	by	ADP
fcis-10203	144	16	“	"	PUNCT
fcis-10203	144	17	yes	yes	INTJ
fcis-10203	144	18	”	"	PUNCT
fcis-10203	144	19	or	or	CCONJ
fcis-10203	144	20	“	"	PUNCT
fcis-10203	144	21	no	no	INTJ
fcis-10203	144	22	”	"	PUNCT
fcis-10203	144	23	,	,	PUNCT
fcis-10203	144	24	so	so	CCONJ
fcis-10203	144	25	the	the	DET
fcis-10203	144	26	answers	answer	NOUN
fcis-10203	144	27	sometimes	sometimes	ADV
fcis-10203	144	28	given	give	VERB
fcis-10203	144	29	by	by	ADP
fcis-10203	144	30	chatgpt	chatgpt	NOUN
fcis-10203	144	31	is	be	AUX
fcis-10203	144	32	unacceptable	unacceptable	ADJ
fcis-10203	144	33	.	.	PUNCT
fcis-10203	145	1	table	table	NOUN
fcis-10203	145	2	6	6	NUM
fcis-10203	145	3	.	.	PUNCT
fcis-10203	146	1	the	the	DET
fcis-10203	146	2	performance	performance	NOUN
fcis-10203	146	3	of	of	ADP
fcis-10203	146	4	spqa	spqa	NOUN
fcis-10203	146	5	and	and	CCONJ
fcis-10203	146	6	unifiedqa	unifiedqa	NOUN
fcis-10203	146	7	-	-	PUNCT
fcis-10203	146	8	v2	v2	NOUN
fcis-10203	146	9	compared	compare	VERB
fcis-10203	146	10	with	with	ADP
fcis-10203	146	11	chatgpt	chatgpt	NOUN
fcis-10203	146	12	situation	situation	NOUN
fcis-10203	146	13	puzzle	puzzle	NOUN
fcis-10203	146	14	with	with	ADP
fcis-10203	146	15	crime	crime	NOUN
fcis-10203	146	16	situation	situation	NOUN
fcis-10203	146	17	puzzle	puzzle	NOUN
fcis-10203	146	18	without	without	ADP
fcis-10203	146	19	crime	crime	NOUN
fcis-10203	146	20	chatgpt	chatgpt	NOUN
fcis-10203	146	21	refuse	refuse	VERB
fcis-10203	146	22	to	to	PART
fcis-10203	146	23	answer	answer	VERB
fcis-10203	146	24	“	"	PUNCT
fcis-10203	146	25	the	the	DET
fcis-10203	146	26	information	information	NOUN
fcis-10203	146	27	given	give	VERB
fcis-10203	146	28	is	be	AUX
fcis-10203	146	29	not	not	PART
fcis-10203	146	30	clear	clear	ADJ
fcis-10203	146	31	”	"	PUNCT
fcis-10203	146	32	“	"	PUNCT
fcis-10203	146	33	not	not	PART
fcis-10203	146	34	mentioned	mention	VERB
fcis-10203	146	35	in	in	ADP
fcis-10203	146	36	the	the	DET
fcis-10203	146	37	story	story	NOUN
fcis-10203	146	38	”	"	PUNCT
fcis-10203	146	39	if	if	SCONJ
fcis-10203	146	40	it	it	PRON
fcis-10203	146	41	answered	answer	VERB
fcis-10203	146	42	,	,	PUNCT
fcis-10203	146	43	the	the	DET
fcis-10203	146	44	answers	answer	NOUN
fcis-10203	146	45	are	be	AUX
fcis-10203	146	46	right	right	ADJ
fcis-10203	146	47	.	.	PUNCT
fcis-10203	147	1	unifiedqa	unifiedqa	NOUN
fcis-10203	147	2	-	-	PUNCT
fcis-10203	147	3	v2	v2	NOUN
fcis-10203	147	4	(	(	PUNCT
fcis-10203	147	5	small	small	ADJ
fcis-10203	147	6	)	)	PUNCT
fcis-10203	147	7	still	still	ADV
fcis-10203	147	8	have	have	VERB
fcis-10203	147	9	wrong	wrong	ADJ
fcis-10203	147	10	answers	answer	NOUN
fcis-10203	147	11	neither	neither	CCONJ
fcis-10203	147	12	“	"	PUNCT
fcis-10203	147	13	yes	yes	INTJ
fcis-10203	147	14	”	"	PUNCT
fcis-10203	147	15	nor	nor	CCONJ
fcis-10203	147	16	“	"	PUNCT
fcis-10203	147	17	no	no	INTJ
fcis-10203	147	18	”	"	PUNCT
fcis-10203	147	19	(	(	PUNCT
fcis-10203	147	20	small	small	ADJ
fcis-10203	147	21	)	)	PUNCT
fcis-10203	147	22	still	still	ADV
fcis-10203	147	23	have	have	VERB
fcis-10203	147	24	wrong	wrong	ADJ
fcis-10203	147	25	answers	answer	NOUN
fcis-10203	147	26	neither	neither	CCONJ
fcis-10203	147	27	“	"	PUNCT
fcis-10203	147	28	yes	yes	INTJ
fcis-10203	147	29	”	"	PUNCT
fcis-10203	147	30	nor	nor	CCONJ
fcis-10203	147	31	“	"	PUNCT
fcis-10203	147	32	no	no	DET
fcis-10203	147	33	”	"	PUNCT
fcis-10203	147	34	spqa	spqa	NOUN
fcis-10203	147	35	judge	judge	VERB
fcis-10203	147	36	the	the	DET
fcis-10203	147	37	task	task	NOUN
fcis-10203	147	38	without	without	ADP
fcis-10203	147	39	mistakes	mistake	NOUN
fcis-10203	147	40	,	,	PUNCT
fcis-10203	147	41	but	but	CCONJ
fcis-10203	147	42	the	the	DET
fcis-10203	147	43	correct	correct	ADJ
fcis-10203	147	44	rate	rate	NOUN
fcis-10203	147	45	is	be	AUX
fcis-10203	147	46	properly	properly	ADV
fcis-10203	147	47	the	the	DET
fcis-10203	147	48	same	same	ADJ
fcis-10203	147	49	as	as	ADP
fcis-10203	147	50	unifiedqa	unifiedqa	NOUN
fcis-10203	147	51	-	-	PUNCT
fcis-10203	147	52	v2	v2	NOUN
fcis-10203	147	53	.	.	PUNCT
fcis-10203	148	1	on	on	ADP
fcis-10203	148	2	situation	situation	NOUN
fcis-10203	148	3	puzzle	puzzle	NOUN
fcis-10203	148	4	questions	question	NOUN
fcis-10203	148	5	,	,	PUNCT
fcis-10203	148	6	the	the	DET
fcis-10203	148	7	performance	performance	NOUN
fcis-10203	148	8	is	be	AUX
fcis-10203	148	9	faster	fast	ADJ
fcis-10203	148	10	to	to	PART
fcis-10203	148	11	reach	reach	VERB
fcis-10203	148	12	the	the	DET
fcis-10203	148	13	target	target	NOUN
fcis-10203	148	14	.	.	PUNCT
fcis-10203	149	1	judge	judge	VERB
fcis-10203	149	2	the	the	DET
fcis-10203	149	3	task	task	NOUN
fcis-10203	149	4	without	without	ADP
fcis-10203	149	5	mistakes	mistake	NOUN
fcis-10203	149	6	,	,	PUNCT
fcis-10203	149	7	but	but	CCONJ
fcis-10203	149	8	the	the	DET
fcis-10203	149	9	correct	correct	ADJ
fcis-10203	149	10	rate	rate	NOUN
fcis-10203	149	11	is	be	AUX
fcis-10203	149	12	properly	properly	ADV
fcis-10203	149	13	the	the	DET
fcis-10203	149	14	same	same	ADJ
fcis-10203	149	15	as	as	ADP
fcis-10203	149	16	unifiedqa	unifiedqa	NOUN
fcis-10203	149	17	-	-	PUNCT
fcis-10203	149	18	v2	v2	NOUN
fcis-10203	149	19	.	.	PUNCT
fcis-10203	150	1	on	on	ADP
fcis-10203	150	2	situation	situation	NOUN
fcis-10203	150	3	puzzle	puzzle	NOUN
fcis-10203	150	4	questions	question	NOUN
fcis-10203	150	5	,	,	PUNCT
fcis-10203	150	6	the	the	DET
fcis-10203	150	7	performance	performance	NOUN
fcis-10203	150	8	is	be	AUX
fcis-10203	150	9	faster	fast	ADJ
fcis-10203	150	10	to	to	PART
fcis-10203	150	11	reach	reach	VERB
fcis-10203	150	12	the	the	DET
fcis-10203	150	13	target	target	NOUN
fcis-10203	150	14	.	.	PUNCT
fcis-10203	151	1	5	5	X
fcis-10203	151	2	.	.	X
fcis-10203	151	3	conclusion	conclusion	NOUN
fcis-10203	151	4	in	in	ADP
fcis-10203	151	5	this	this	DET
fcis-10203	151	6	paper	paper	NOUN
fcis-10203	151	7	,	,	PUNCT
fcis-10203	151	8	i	i	PRON
fcis-10203	151	9	used	use	VERB
fcis-10203	151	10	the	the	DET
fcis-10203	151	11	t5	t5	PROPN
fcis-10203	151	12	architecture	architecture	NOUN
fcis-10203	151	13	,	,	PUNCT
fcis-10203	151	14	like	like	ADP
fcis-10203	151	15	unifiedqa	unifiedqa	PROPN
fcis-10203	151	16	’s	’s	PART
fcis-10203	151	17	structure	structure	NOUN
fcis-10203	151	18	,	,	PUNCT
fcis-10203	151	19	trained	train	VERB
fcis-10203	151	20	and	and	CCONJ
fcis-10203	151	21	fine	fine	ADV
fcis-10203	151	22	-	-	PUNCT
fcis-10203	151	23	tuned	tune	VERB
fcis-10203	151	24	a	a	DET
fcis-10203	151	25	new	new	ADJ
fcis-10203	151	26	model	model	NOUN
fcis-10203	151	27	named	name	VERB
fcis-10203	151	28	spqa	spqa	PROPN
fcis-10203	151	29	to	to	PART
fcis-10203	151	30	answer	answer	VERB
fcis-10203	151	31	situation	situation	NOUN
fcis-10203	151	32	puzzle	puzzle	NOUN
fcis-10203	151	33	questions	question	NOUN
fcis-10203	151	34	to	to	PART
fcis-10203	151	35	reach	reach	VERB
fcis-10203	151	36	the	the	DET
fcis-10203	151	37	requests	request	NOUN
fcis-10203	151	38	by	by	ADP
fcis-10203	151	39	adding	add	VERB
fcis-10203	151	40	a	a	DET
fcis-10203	151	41	new	new	ADJ
fcis-10203	151	42	dataset	dataset	NOUN
fcis-10203	151	43	spq	spq	NOUN
fcis-10203	151	44	on	on	ADP
fcis-10203	151	45	tpu	tpu	NOUN
fcis-10203	151	46	-	-	PUNCT
fcis-10203	151	47	v2.8	v2.8	NOUN
fcis-10203	151	48	.	.	PUNCT
fcis-10203	152	1	in	in	ADP
fcis-10203	152	2	addition	addition	NOUN
fcis-10203	152	3	,	,	PUNCT
fcis-10203	152	4	i	i	PRON
fcis-10203	152	5	used	use	VERB
fcis-10203	152	6	less	less	ADJ
fcis-10203	152	7	datasets	dataset	NOUN
fcis-10203	152	8	on	on	ADP
fcis-10203	152	9	gpu	gpu	NOUN
fcis-10203	152	10	to	to	PART
fcis-10203	152	11	train	train	VERB
fcis-10203	152	12	and	and	CCONJ
fcis-10203	152	13	prompt	prompt	ADV
fcis-10203	152	14	-	-	PUNCT
fcis-10203	152	15	tuned	tune	VERB
fcis-10203	152	16	a	a	DET
fcis-10203	152	17	new	new	ADJ
fcis-10203	152	18	model	model	NOUN
fcis-10203	152	19	titled	title	VERB
fcis-10203	152	20	spqa	spqa	NOUN
fcis-10203	152	21	-	-	PUNCT
fcis-10203	152	22	prompt	prompt	NOUN
fcis-10203	152	23	to	to	PART
fcis-10203	152	24	answer	answer	VERB
fcis-10203	152	25	situation	situation	NOUN
fcis-10203	152	26	puzzle	puzzle	NOUN
fcis-10203	152	27	questions	question	NOUN
fcis-10203	152	28	under	under	ADP
fcis-10203	152	29	the	the	DET
fcis-10203	152	30	same	same	ADJ
fcis-10203	152	31	dataset	dataset	NOUN
fcis-10203	152	32	scale	scale	NOUN
fcis-10203	152	33	.	.	PUNCT
fcis-10203	153	1	according	accord	VERB
fcis-10203	153	2	to	to	ADP
fcis-10203	153	3	the	the	DET
fcis-10203	153	4	performance	performance	NOUN
fcis-10203	153	5	as	as	ADP
fcis-10203	153	6	above	above	ADV
fcis-10203	153	7	,	,	PUNCT
fcis-10203	153	8	the	the	DET
fcis-10203	153	9	conclusion	conclusion	NOUN
fcis-10203	153	10	is	be	AUX
fcis-10203	153	11	that	that	SCONJ
fcis-10203	153	12	situation	situation	NOUN
fcis-10203	153	13	puzzle	puzzle	NOUN
fcis-10203	153	14	datasets	dataset	NOUN
fcis-10203	153	15	(	(	PUNCT
fcis-10203	153	16	spq	spq	NOUN
fcis-10203	153	17	)	)	PUNCT
fcis-10203	153	18	is	be	AUX
fcis-10203	153	19	important	important	ADJ
fcis-10203	153	20	for	for	ADP
fcis-10203	153	21	fine	fine	ADV
fcis-10203	153	22	-	-	PUNCT
fcis-10203	153	23	tuning	tuning	NOUN
fcis-10203	153	24	and	and	CCONJ
fcis-10203	153	25	prompt	prompt	NOUN
fcis-10203	153	26	-	-	PUNCT
fcis-10203	153	27	tuning	tuning	NOUN
fcis-10203	153	28	to	to	PART
fcis-10203	153	29	answer	answer	VERB
fcis-10203	153	30	situation	situation	NOUN
fcis-10203	153	31	puzzle	puzzle	NOUN
fcis-10203	153	32	questions	question	NOUN
fcis-10203	153	33	,	,	PUNCT
fcis-10203	153	34	but	but	CCONJ
fcis-10203	153	35	also	also	ADV
fcis-10203	153	36	make	make	VERB
fcis-10203	153	37	the	the	DET
fcis-10203	153	38	answering	answering	NOUN
fcis-10203	153	39	ability	ability	NOUN
fcis-10203	153	40	of	of	ADP
fcis-10203	153	41	normal	normal	ADJ
fcis-10203	153	42	yes	yes	INTJ
fcis-10203	153	43	/	/	SYM
fcis-10203	153	44	no	no	DET
fcis-10203	153	45	questions	question	NOUN
fcis-10203	153	46	worse	bad	ADJ
fcis-10203	153	47	.	.	PUNCT
fcis-10203	154	1	for	for	ADP
fcis-10203	154	2	fine	fine	ADV
fcis-10203	154	3	-	-	PUNCT
fcis-10203	154	4	tuning	tuning	NOUN
fcis-10203	154	5	,	,	PUNCT
fcis-10203	154	6	the	the	DET
fcis-10203	154	7	performance	performance	NOUN
fcis-10203	154	8	of	of	ADP
fcis-10203	154	9	spqa	spqa	NOUN
fcis-10203	154	10	for	for	ADP
fcis-10203	154	11	‘	'	PUNCT
fcis-10203	154	12	3b	3b	NUM
fcis-10203	154	13	’	'	PUNCT
fcis-10203	154	14	is	be	AUX
fcis-10203	154	15	good	good	ADJ
fcis-10203	154	16	enough	enough	ADV
fcis-10203	154	17	at	at	ADP
fcis-10203	154	18	the	the	DET
fcis-10203	154	19	current	current	ADJ
fcis-10203	154	20	stage	stage	NOUN
fcis-10203	154	21	.	.	PUNCT
fcis-10203	155	1	however	however	ADV
fcis-10203	155	2	,	,	PUNCT
fcis-10203	155	3	because	because	SCONJ
fcis-10203	155	4	of	of	ADP
fcis-10203	155	5	tpu	tpu	NOUN
fcis-10203	155	6	and	and	CCONJ
fcis-10203	155	7	gpu	gpu	PROPN
fcis-10203	155	8	limit	limit	NOUN
fcis-10203	155	9	and	and	CCONJ
fcis-10203	155	10	the	the	DET
fcis-10203	155	11	data	data	NOUN
fcis-10203	155	12	scales	scale	NOUN
fcis-10203	155	13	of	of	ADP
fcis-10203	155	14	spq	spq	NOUN
fcis-10203	155	15	dataset	dataset	NOUN
fcis-10203	155	16	,	,	PUNCT
fcis-10203	155	17	the	the	DET
fcis-10203	155	18	effects	effect	NOUN
fcis-10203	155	19	of	of	ADP
fcis-10203	155	20	larger	large	ADJ
fcis-10203	155	21	spq	spq	NOUN
fcis-10203	155	22	dataset	dataset	NOUN
fcis-10203	155	23	,	,	PUNCT
fcis-10203	155	24	data	datum	NOUN
fcis-10203	155	25	scale	scale	NOUN
fcis-10203	155	26	of	of	ADP
fcis-10203	155	27	‘	'	PUNCT
fcis-10203	155	28	11b	11b	NOUN
fcis-10203	155	29	’	'	PUNCT
fcis-10203	155	30	and	and	CCONJ
fcis-10203	155	31	prompt	prompt	NOUN
fcis-10203	155	32	-	-	PUNCT
fcis-10203	155	33	tuning	tuning	NOUN
fcis-10203	155	34	with	with	ADP
fcis-10203	155	35	larger	large	ADJ
fcis-10203	155	36	datasets	dataset	NOUN
fcis-10203	155	37	can	can	AUX
fcis-10203	155	38	not	not	PART
fcis-10203	155	39	considered	consider	VERB
fcis-10203	155	40	in	in	ADP
fcis-10203	155	41	this	this	DET
fcis-10203	155	42	paper	paper	NOUN
fcis-10203	155	43	.	.	PUNCT
fcis-10203	156	1	in	in	ADP
fcis-10203	156	2	addition	addition	NOUN
fcis-10203	156	3	,	,	PUNCT
fcis-10203	156	4	the	the	DET
fcis-10203	156	5	performance	performance	NOUN
fcis-10203	156	6	and	and	CCONJ
fcis-10203	156	7	gains	gain	NOUN
fcis-10203	156	8	are	be	AUX
fcis-10203	156	9	not	not	PART
fcis-10203	156	10	quite	quite	ADV
fcis-10203	156	11	uniform	uniform	ADJ
fcis-10203	156	12	for	for	ADP
fcis-10203	156	13	all	all	DET
fcis-10203	156	14	datasets	dataset	NOUN
fcis-10203	156	15	.	.	PUNCT
fcis-10203	157	1	i	i	PRON
fcis-10203	157	2	will	will	AUX
fcis-10203	157	3	try	try	VERB
fcis-10203	157	4	to	to	PART
fcis-10203	157	5	build	build	VERB
fcis-10203	157	6	and	and	CCONJ
fcis-10203	157	7	train	train	VERB
fcis-10203	157	8	the	the	DET
fcis-10203	157	9	model	model	NOUN
fcis-10203	157	10	only	only	ADV
fcis-10203	157	11	with	with	ADP
fcis-10203	157	12	large	large	ADJ
fcis-10203	157	13	spq	spq	NOUN
fcis-10203	157	14	dataset	dataset	NOUN
fcis-10203	157	15	and	and	CCONJ
fcis-10203	157	16	other	other	ADJ
fcis-10203	157	17	further	further	ADJ
fcis-10203	157	18	research	research	NOUN
fcis-10203	157	19	in	in	ADP
fcis-10203	157	20	the	the	DET
fcis-10203	157	21	future	future	ADJ
fcis-10203	157	22	work	work	NOUN
fcis-10203	157	23	.	.	PUNCT
fcis-10203	158	1	acknowledgments	acknowledgment	NOUN
fcis-10203	158	2	tpu	tpu	VERB
fcis-10203	158	3	for	for	ADP
fcis-10203	158	4	performing	perform	VERB
fcis-10203	158	5	experiments	experiment	NOUN
fcis-10203	158	6	and	and	CCONJ
fcis-10203	158	7	researches	research	NOUN
fcis-10203	158	8	were	be	AUX
fcis-10203	158	9	provided	provide	VERB
fcis-10203	158	10	by	by	ADP
fcis-10203	158	11	gcps	gcp	NOUN
fcis-10203	158	12	of	of	ADP
fcis-10203	158	13	google	google	PROPN
fcis-10203	158	14	.	.	PUNCT
fcis-10203	159	1	references	reference	NOUN
fcis-10203	159	2	[	[	X
fcis-10203	159	3	1	1	NUM
fcis-10203	159	4	]	]	PUNCT
fcis-10203	159	5	jed	jed	PROPN
fcis-10203	159	6	hartman	hartman	PROPN
fcis-10203	159	7	,	,	PUNCT
fcis-10203	159	8	rec.puzzles	rec.puzzle	NOUN
fcis-10203	159	9	archive	archive	NOUN
fcis-10203	159	10	27	27	NUM
fcis-10203	159	11	aug	aug	PROPN
fcis-10203	159	12	1998	1998	NUM
fcis-10203	160	1	http://www	http://www	PROPN
fcis-10203	160	2	.	.	PUNCT
fcis-10203	160	3	kith.org/logos/things/sitpuz/lateral.html	kith.org/logos/things/sitpuz/lateral.html	PROPN
fcis-10203	160	4	.	.	PUNCT
fcis-10203	161	1	[	[	X
fcis-10203	161	2	2	2	NUM
fcis-10203	161	3	]	]	X
fcis-10203	161	4	vaswani	vaswani	NOUN
fcis-10203	161	5	,	,	PUNCT
fcis-10203	161	6	a.	a.	NOUN
fcis-10203	161	7	,	,	PUNCT
fcis-10203	161	8	shazeer	shazeer	NOUN
fcis-10203	161	9	,	,	PUNCT
fcis-10203	161	10	n.	n.	NOUN
fcis-10203	161	11	,	,	PUNCT
fcis-10203	161	12	parmar	parmar	PROPN
fcis-10203	161	13	,	,	PUNCT
fcis-10203	161	14	n.	n.	NOUN
fcis-10203	161	15	,	,	PUNCT
fcis-10203	161	16	uszkoreit	uszkoreit	PROPN
fcis-10203	161	17	,	,	PUNCT
fcis-10203	161	18	j.	j.	PROPN
fcis-10203	161	19	,	,	PUNCT
fcis-10203	161	20	jones	jones	PROPN
fcis-10203	161	21	,	,	PUNCT
fcis-10203	161	22	l.	l.	PROPN
fcis-10203	161	23	,	,	PUNCT
fcis-10203	161	24	gomez	gomez	PROPN
fcis-10203	161	25	,	,	PUNCT
fcis-10203	161	26	a.	a.	NOUN
fcis-10203	161	27	n.	n.	PROPN
fcis-10203	161	28	,	,	PUNCT
fcis-10203	161	29	...	...	PUNCT
fcis-10203	161	30	&	&	CCONJ
fcis-10203	161	31	polosukhin	polosukhin	PROPN
fcis-10203	161	32	,	,	PUNCT
fcis-10203	161	33	i.	i.	NOUN
fcis-10203	161	34	(	(	PUNCT
fcis-10203	161	35	2017	2017	NUM
fcis-10203	161	36	)	)	PUNCT
fcis-10203	161	37	.	.	PUNCT
fcis-10203	162	1	attention	attention	NOUN
fcis-10203	162	2	is	be	AUX
fcis-10203	162	3	all	all	PRON
fcis-10203	162	4	you	you	PRON
fcis-10203	162	5	need	need	VERB
fcis-10203	162	6	.	.	PUNCT
fcis-10203	163	1	advances	advance	NOUN
fcis-10203	163	2	in	in	ADP
fcis-10203	163	3	neural	neural	ADJ
fcis-10203	163	4	information	information	NOUN
fcis-10203	163	5	processing	processing	NOUN
fcis-10203	163	6	systems	system	NOUN
fcis-10203	163	7	,	,	PUNCT
fcis-10203	163	8	30	30	NUM
fcis-10203	163	9	.	.	PUNCT
fcis-10203	164	1	[	[	X
fcis-10203	164	2	3	3	NUM
fcis-10203	164	3	]	]	X
fcis-10203	164	4	radford	radford	PROPN
fcis-10203	164	5	,	,	PUNCT
fcis-10203	164	6	a.	a.	PROPN
fcis-10203	164	7	,	,	PUNCT
fcis-10203	164	8	narasimhan	narasimhan	PROPN
fcis-10203	164	9	,	,	PUNCT
fcis-10203	164	10	k.	k.	PROPN
fcis-10203	164	11	,	,	PUNCT
fcis-10203	164	12	salimans	saliman	NOUN
fcis-10203	164	13	,	,	PUNCT
fcis-10203	164	14	t.	t.	PROPN
fcis-10203	164	15	,	,	PUNCT
fcis-10203	164	16	&	&	CCONJ
fcis-10203	164	17	sutskever	sutskever	PROPN
fcis-10203	164	18	,	,	PUNCT
fcis-10203	164	19	i.	i.	PROPN
fcis-10203	164	20	(	(	PUNCT
fcis-10203	164	21	2018	2018	NUM
fcis-10203	164	22	)	)	PUNCT
fcis-10203	164	23	.	.	PUNCT
fcis-10203	165	1	improving	improve	VERB
fcis-10203	165	2	language	language	NOUN
fcis-10203	165	3	understanding	understanding	NOUN
fcis-10203	165	4	by	by	ADP
fcis-10203	165	5	generative	generative	ADJ
fcis-10203	165	6	pretraining	pretraining	NOUN
fcis-10203	165	7	.	.	PUNCT
fcis-10203	166	1	[	[	X
fcis-10203	166	2	4	4	NUM
fcis-10203	166	3	]	]	X
fcis-10203	166	4	devlin	devlin	PROPN
fcis-10203	166	5	,	,	PUNCT
fcis-10203	166	6	j.	j.	PROPN
fcis-10203	166	7	,	,	PUNCT
fcis-10203	166	8	chang	chang	PROPN
fcis-10203	166	9	,	,	PUNCT
fcis-10203	166	10	m.	m.	PROPN
fcis-10203	166	11	w.	w.	PROPN
fcis-10203	166	12	,	,	PUNCT
fcis-10203	166	13	lee	lee	PROPN
fcis-10203	166	14	,	,	PUNCT
fcis-10203	166	15	k.	k.	PROPN
fcis-10203	166	16	,	,	PUNCT
fcis-10203	166	17	&	&	CCONJ
fcis-10203	166	18	toutanova	toutanova	PROPN
fcis-10203	166	19	,	,	PUNCT
fcis-10203	166	20	k.	k.	PROPN
fcis-10203	166	21	(	(	PUNCT
fcis-10203	166	22	2018	2018	NUM
fcis-10203	166	23	)	)	PUNCT
fcis-10203	166	24	.	.	PUNCT
fcis-10203	167	1	bert	bert	PROPN
fcis-10203	167	2	:	:	PUNCT
fcis-10203	167	3	pre	pre	ADJ
fcis-10203	167	4	-	-	NOUN
fcis-10203	167	5	training	training	NOUN
fcis-10203	167	6	of	of	ADP
fcis-10203	167	7	deep	deep	ADJ
fcis-10203	167	8	bidirectional	bidirectional	ADJ
fcis-10203	167	9	transformers	transformer	NOUN
fcis-10203	167	10	for	for	ADP
fcis-10203	167	11	language	language	NOUN
fcis-10203	167	12	understanding	understanding	NOUN
fcis-10203	167	13	.	.	PUNCT
fcis-10203	168	1	arxiv	arxiv	PROPN
fcis-10203	168	2	preprint	preprint	PROPN
fcis-10203	168	3	arxiv:1810.04805	arxiv:1810.04805	PROPN
fcis-10203	168	4	.	.	PUNCT
fcis-10203	169	1	[	[	X
fcis-10203	169	2	5	5	NUM
fcis-10203	169	3	]	]	X
fcis-10203	169	4	mccann	mccann	PROPN
fcis-10203	169	5	,	,	PUNCT
fcis-10203	169	6	b.	b.	PROPN
fcis-10203	169	7	,	,	PUNCT
fcis-10203	169	8	keskar	keskar	PROPN
fcis-10203	169	9	,	,	PUNCT
fcis-10203	169	10	n.	n.	PROPN
fcis-10203	169	11	s.	s.	PROPN
fcis-10203	169	12	,	,	PUNCT
fcis-10203	169	13	xiong	xiong	PROPN
fcis-10203	169	14	,	,	PUNCT
fcis-10203	169	15	c.	c.	PROPN
fcis-10203	169	16	,	,	PUNCT
fcis-10203	169	17	&	&	CCONJ
fcis-10203	169	18	socher	socher	PROPN
fcis-10203	169	19	,	,	PUNCT
fcis-10203	169	20	r.	r.	PROPN
fcis-10203	169	21	(	(	PUNCT
fcis-10203	169	22	2018	2018	NUM
fcis-10203	169	23	)	)	PUNCT
fcis-10203	169	24	.	.	PUNCT
fcis-10203	170	1	the	the	DET
fcis-10203	170	2	natural	natural	ADJ
fcis-10203	170	3	language	language	NOUN
fcis-10203	170	4	decathlon	decathlon	NOUN
fcis-10203	170	5	:	:	PUNCT
fcis-10203	170	6	multitask	multitask	PROPN
fcis-10203	170	7	learning	learning	NOUN
fcis-10203	170	8	as	as	ADP
fcis-10203	170	9	question	question	NOUN
fcis-10203	170	10	answering	answering	NOUN
fcis-10203	170	11	.	.	PUNCT
fcis-10203	171	1	arxiv	arxiv	PROPN
fcis-10203	171	2	preprint	preprint	VERB
fcis-10203	171	3	arxiv:1806.08730	arxiv:1806.08730	ADV
fcis-10203	171	4	.	.	PUNCT
fcis-10203	172	1	[	[	X
fcis-10203	172	2	6	6	NUM
fcis-10203	172	3	]	]	SYM
fcis-10203	172	4	yu	yu	PROPN
fcis-10203	172	5	,	,	PUNCT
fcis-10203	172	6	j.	j.	PROPN
fcis-10203	172	7	,	,	PUNCT
fcis-10203	172	8	lin	lin	PROPN
fcis-10203	172	9	,	,	PUNCT
fcis-10203	172	10	z.	z.	PROPN
fcis-10203	172	11	,	,	PUNCT
fcis-10203	172	12	yang	yang	PROPN
fcis-10203	172	13	,	,	PUNCT
fcis-10203	172	14	j.	j.	PROPN
fcis-10203	172	15	,	,	PUNCT
fcis-10203	172	16	shen	shen	PROPN
fcis-10203	172	17	,	,	PUNCT
fcis-10203	172	18	x.	x.	PROPN
fcis-10203	172	19	,	,	PUNCT
fcis-10203	172	20	lu	lu	PROPN
fcis-10203	172	21	,	,	PUNCT
fcis-10203	172	22	x.	x.	PROPN
fcis-10203	172	23	,	,	PUNCT
fcis-10203	172	24	&	&	CCONJ
fcis-10203	172	25	huang	huang	PROPN
fcis-10203	172	26	,	,	PUNCT
fcis-10203	172	27	t.	t.	PROPN
fcis-10203	172	28	s.	s.	PROPN
fcis-10203	172	29	(	(	PUNCT
fcis-10203	172	30	2018	2018	NUM
fcis-10203	172	31	)	)	PUNCT
fcis-10203	172	32	.	.	PUNCT
fcis-10203	173	1	generative	generative	ADJ
fcis-10203	173	2	image	image	NOUN
fcis-10203	173	3	inpainting	inpainte	VERB
fcis-10203	173	4	with	with	ADP
fcis-10203	173	5	contextual	contextual	ADJ
fcis-10203	173	6	attention	attention	NOUN
fcis-10203	173	7	.	.	PUNCT
fcis-10203	174	1	in	in	ADP
fcis-10203	174	2	proceedings	proceeding	NOUN
fcis-10203	174	3	of	of	ADP
fcis-10203	174	4	the	the	DET
fcis-10203	174	5	ieee	ieee	NOUN
fcis-10203	174	6	conference	conference	NOUN
fcis-10203	174	7	on	on	ADP
fcis-10203	174	8	computer	computer	NOUN
fcis-10203	174	9	vision	vision	NOUN
fcis-10203	174	10	and	and	CCONJ
fcis-10203	174	11	pattern	pattern	NOUN
fcis-10203	174	12	recognition	recognition	NOUN
fcis-10203	174	13	(	(	PUNCT
fcis-10203	174	14	pp	pp	ADJ
fcis-10203	174	15	.	.	PUNCT
fcis-10203	175	1	5505	5505	NUM
fcis-10203	175	2	-	-	SYM
fcis-10203	175	3	5514	5514	NUM
fcis-10203	175	4	)	)	PUNCT
fcis-10203	175	5	.	.	PUNCT
fcis-10203	176	1	[	[	X
fcis-10203	176	2	7	7	NUM
fcis-10203	176	3	]	]	X
fcis-10203	176	4	devlin	devlin	PROPN
fcis-10203	176	5	,	,	PUNCT
fcis-10203	176	6	j.	j.	PROPN
fcis-10203	176	7	,	,	PUNCT
fcis-10203	176	8	chang	chang	PROPN
fcis-10203	176	9	,	,	PUNCT
fcis-10203	176	10	m.	m.	PROPN
fcis-10203	176	11	w.	w.	PROPN
fcis-10203	176	12	,	,	PUNCT
fcis-10203	176	13	lee	lee	PROPN
fcis-10203	176	14	,	,	PUNCT
fcis-10203	176	15	k.	k.	PROPN
fcis-10203	176	16	,	,	PUNCT
fcis-10203	176	17	&	&	CCONJ
fcis-10203	176	18	toutanova	toutanova	PROPN
fcis-10203	176	19	,	,	PUNCT
fcis-10203	176	20	k.	k.	PROPN
fcis-10203	176	21	(	(	PUNCT
fcis-10203	176	22	2018	2018	NUM
fcis-10203	176	23	)	)	PUNCT
fcis-10203	176	24	.	.	PUNCT
fcis-10203	177	1	bert	bert	PROPN
fcis-10203	177	2	:	:	PUNCT
fcis-10203	177	3	pre	pre	ADJ
fcis-10203	177	4	-	-	NOUN
fcis-10203	177	5	training	training	NOUN
fcis-10203	177	6	of	of	ADP
fcis-10203	177	7	deep	deep	ADJ
fcis-10203	177	8	bidirectional	bidirectional	ADJ
fcis-10203	177	9	transformers	transformer	NOUN
fcis-10203	177	10	for	for	ADP
fcis-10203	177	11	language	language	NOUN
fcis-10203	177	12	understanding	understanding	NOUN
fcis-10203	177	13	.	.	PUNCT
fcis-10203	178	1	arxiv	arxiv	PROPN
fcis-10203	178	2	preprint	preprint	PROPN
fcis-10203	178	3	arxiv:1810.04805	arxiv:1810.04805	PROPN
fcis-10203	178	4	.	.	PUNCT
fcis-10203	179	1	[	[	X
fcis-10203	179	2	8	8	NUM
fcis-10203	179	3	]	]	X
fcis-10203	179	4	radford	radford	PROPN
fcis-10203	179	5	,	,	PUNCT
fcis-10203	179	6	a.	a.	PROPN
fcis-10203	179	7	,	,	PUNCT
fcis-10203	179	8	&	&	CCONJ
fcis-10203	179	9	salimans	salimans	PROPN
fcis-10203	179	10	,	,	PUNCT
fcis-10203	179	11	t.	t.	PROPN
fcis-10203	179	12	(	(	PUNCT
fcis-10203	179	13	2018	2018	NUM
fcis-10203	179	14	)	)	PUNCT
fcis-10203	179	15	.	.	PUNCT
fcis-10203	180	1	gpt	gpt	NOUN
fcis-10203	180	2	:	:	PUNCT
fcis-10203	180	3	improving	improve	VERB
fcis-10203	180	4	language	language	NOUN
fcis-10203	180	5	understanding	understanding	NOUN
fcis-10203	180	6	by	by	ADP
fcis-10203	180	7	generative	generative	ADJ
fcis-10203	180	8	pre	pre	NOUN
fcis-10203	180	9	-	-	NOUN
fcis-10203	180	10	training	training	NOUN
fcis-10203	180	11	.	.	PUNCT
fcis-10203	181	1	arxiv	arxiv	NOUN
fcis-10203	181	2	.	.	PUNCT
fcis-10203	182	1	[	[	X
fcis-10203	182	2	9	9	NUM
fcis-10203	182	3	]	]	SYM
fcis-10203	182	4	lewis	lewis	PROPN
fcis-10203	182	5	,	,	PUNCT
fcis-10203	182	6	m.	m.	NOUN
fcis-10203	182	7	,	,	PUNCT
fcis-10203	182	8	liu	liu	PROPN
fcis-10203	182	9	,	,	PUNCT
fcis-10203	182	10	y.	y.	PROPN
fcis-10203	182	11	,	,	PUNCT
fcis-10203	182	12	goyal	goyal	PROPN
fcis-10203	182	13	,	,	PUNCT
fcis-10203	182	14	n.	n.	NOUN
fcis-10203	182	15	,	,	PUNCT
fcis-10203	182	16	ghazvininejad	ghazvininejad	PROPN
fcis-10203	182	17	,	,	PUNCT
fcis-10203	182	18	m.	m.	NOUN
fcis-10203	182	19	,	,	PUNCT
fcis-10203	182	20	mohamed	mohamed	PROPN
fcis-10203	182	21	,	,	PUNCT
fcis-10203	182	22	a.	a.	NOUN
fcis-10203	182	23	,	,	PUNCT
fcis-10203	182	24	levy	levy	NOUN
fcis-10203	182	25	,	,	PUNCT
fcis-10203	182	26	o.	o.	PROPN
fcis-10203	182	27	,	,	PUNCT
fcis-10203	182	28	...	...	PUNCT
fcis-10203	182	29	&	&	CCONJ
fcis-10203	182	30	zettlemoyer	zettlemoyer	NOUN
fcis-10203	182	31	,	,	PUNCT
fcis-10203	182	32	l.	l.	PROPN
fcis-10203	182	33	(	(	PUNCT
fcis-10203	182	34	2019	2019	NUM
fcis-10203	182	35	)	)	PUNCT
fcis-10203	182	36	.	.	PUNCT
fcis-10203	183	1	bart	bart	PROPN
fcis-10203	183	2	:	:	PUNCT
fcis-10203	183	3	denoising	denoise	VERB
fcis-10203	183	4	sequence	sequence	NOUN
fcis-10203	183	5	-	-	PUNCT
fcis-10203	183	6	to	to	ADP
fcis-10203	183	7	-	-	PUNCT
fcis-10203	183	8	sequence	sequence	NOUN
fcis-10203	183	9	pre	pre	NOUN
fcis-10203	183	10	-	-	NOUN
fcis-10203	183	11	training	training	NOUN
fcis-10203	183	12	for	for	ADP
fcis-10203	183	13	natural	natural	ADJ
fcis-10203	183	14	language	language	NOUN
fcis-10203	183	15	generation	generation	NOUN
fcis-10203	183	16	,	,	PUNCT
fcis-10203	183	17	translation	translation	NOUN
fcis-10203	183	18	,	,	PUNCT
fcis-10203	183	19	and	and	CCONJ
fcis-10203	183	20	comprehension	comprehension	NOUN
fcis-10203	183	21	.	.	PUNCT
fcis-10203	184	1	arxiv	arxiv	PROPN
fcis-10203	184	2	preprint	preprint	NOUN
fcis-10203	184	3	arxiv:1910.13461	arxiv:1910.13461	ADV
fcis-10203	184	4	.	.	PUNCT
fcis-10203	185	1	[	[	X
fcis-10203	185	2	10	10	NUM
fcis-10203	185	3	]	]	X
fcis-10203	185	4	zhang	zhang	PROPN
fcis-10203	185	5	,	,	PUNCT
fcis-10203	185	6	y.	y.	PROPN
fcis-10203	185	7	,	,	PUNCT
fcis-10203	185	8	sun	sun	PROPN
fcis-10203	185	9	,	,	PUNCT
fcis-10203	185	10	s.	s.	PROPN
fcis-10203	185	11	,	,	PUNCT
fcis-10203	185	12	galley	galley	NOUN
fcis-10203	185	13	,	,	PUNCT
fcis-10203	185	14	m.	m.	NOUN
fcis-10203	185	15	,	,	PUNCT
fcis-10203	185	16	chen	chen	PROPN
fcis-10203	185	17	,	,	PUNCT
fcis-10203	185	18	y.	y.	PROPN
fcis-10203	185	19	c.	c.	PROPN
fcis-10203	185	20	,	,	PUNCT
fcis-10203	185	21	brockett	brockett	PROPN
fcis-10203	185	22	,	,	PUNCT
fcis-10203	185	23	c.	c.	PROPN
fcis-10203	185	24	,	,	PUNCT
fcis-10203	185	25	gao	gao	PROPN
fcis-10203	185	26	,	,	PUNCT
fcis-10203	185	27	x.	x.	NOUN
fcis-10203	185	28	,	,	PUNCT
fcis-10203	185	29	...	...	PUNCT
fcis-10203	185	30	&	&	CCONJ
fcis-10203	185	31	dolan	dolan	PROPN
fcis-10203	185	32	,	,	PUNCT
fcis-10203	185	33	b.	b.	PROPN
fcis-10203	185	34	(	(	PUNCT
fcis-10203	185	35	2019	2019	NUM
fcis-10203	185	36	)	)	PUNCT
fcis-10203	185	37	.	.	PUNCT
fcis-10203	186	1	dialogpt	dialogpt	NOUN
fcis-10203	186	2	:	:	PUNCT
fcis-10203	186	3	large	large	ADJ
fcis-10203	186	4	-	-	PUNCT
fcis-10203	186	5	scale	scale	NOUN
fcis-10203	186	6	generative	generative	ADJ
fcis-10203	186	7	pre	pre	NOUN
fcis-10203	186	8	-	-	NOUN
fcis-10203	186	9	training	training	NOUN
fcis-10203	186	10	for	for	ADP
fcis-10203	186	11	conversational	conversational	ADJ
fcis-10203	186	12	response	response	NOUN
fcis-10203	186	13	generation	generation	NOUN
fcis-10203	186	14	.	.	PUNCT
fcis-10203	187	1	arxiv	arxiv	PROPN
fcis-10203	187	2	preprint	preprint	NOUN
fcis-10203	187	3	arxiv:1911.00536	arxiv:1911.00536	NOUN
fcis-10203	187	4	.	.	PUNCT
fcis-10203	188	1	[	[	X
fcis-10203	188	2	11	11	NUM
fcis-10203	188	3	]	]	PUNCT
fcis-10203	188	4	raffel	raffel	NOUN
fcis-10203	188	5	,	,	PUNCT
fcis-10203	188	6	c.	c.	NOUN
fcis-10203	188	7	,	,	PUNCT
fcis-10203	188	8	shazeer	shazeer	NOUN
fcis-10203	188	9	,	,	PUNCT
fcis-10203	188	10	n.	n.	NOUN
fcis-10203	188	11	,	,	PUNCT
fcis-10203	188	12	roberts	roberts	PROPN
fcis-10203	188	13	,	,	PUNCT
fcis-10203	188	14	a.	a.	PROPN
fcis-10203	188	15	,	,	PUNCT
fcis-10203	188	16	lee	lee	PROPN
fcis-10203	188	17	,	,	PUNCT
fcis-10203	188	18	k.	k.	PROPN
fcis-10203	188	19	,	,	PUNCT
fcis-10203	188	20	narang	narang	PROPN
fcis-10203	188	21	,	,	PUNCT
fcis-10203	188	22	s.	s.	PROPN
fcis-10203	188	23	,	,	PUNCT
fcis-10203	188	24	matena	matena	PROPN
fcis-10203	188	25	,	,	PUNCT
fcis-10203	188	26	m.	m.	NOUN
fcis-10203	188	27	,	,	PUNCT
fcis-10203	188	28	...	...	PUNCT
fcis-10203	188	29	&	&	CCONJ
fcis-10203	188	30	liu	liu	PROPN
fcis-10203	188	31	,	,	PUNCT
fcis-10203	188	32	p.	p.	PROPN
fcis-10203	188	33	j.	j.	PROPN
fcis-10203	188	34	(	(	PUNCT
fcis-10203	188	35	2020	2020	NUM
fcis-10203	188	36	)	)	PUNCT
fcis-10203	188	37	.	.	PUNCT
fcis-10203	189	1	exploring	explore	VERB
fcis-10203	189	2	the	the	DET
fcis-10203	189	3	limits	limit	NOUN
fcis-10203	189	4	of	of	ADP
fcis-10203	189	5	transfer	transfer	NOUN
fcis-10203	189	6	learning	learn	VERB
fcis-10203	189	7	with	with	ADP
fcis-10203	189	8	a	a	DET
fcis-10203	189	9	unified	unified	ADJ
fcis-10203	189	10	text	text	NOUN
fcis-10203	189	11	-	-	PUNCT
fcis-10203	189	12	to	to	ADP
fcis-10203	189	13	-	-	PUNCT
fcis-10203	189	14	text	text	NOUN
fcis-10203	189	15	transformer	transformer	NOUN
fcis-10203	189	16	.	.	PUNCT
fcis-10203	190	1	j.	j.	PROPN
fcis-10203	190	2	mach	mach	PROPN
fcis-10203	190	3	.	.	PUNCT
fcis-10203	191	1	learn	learn	VERB
fcis-10203	191	2	.	.	PUNCT
fcis-10203	192	1	res	re	NOUN
fcis-10203	192	2	.	.	PROPN
fcis-10203	192	3	,	,	PUNCT
fcis-10203	192	4	21(140	21(140	NUM
fcis-10203	192	5	)	)	PUNCT
fcis-10203	192	6	,	,	PUNCT
fcis-10203	192	7	1	1	NUM
fcis-10203	192	8	-	-	SYM
fcis-10203	192	9	67	67	NUM
fcis-10203	192	10	.	.	PUNCT
fcis-10203	193	1	[	[	X
fcis-10203	193	2	12	12	NUM
fcis-10203	193	3	]	]	X
fcis-10203	193	4	liu	liu	PROPN
fcis-10203	193	5	,	,	PUNCT
fcis-10203	193	6	p.	p.	PROPN
fcis-10203	193	7	,	,	PUNCT
fcis-10203	193	8	yuan	yuan	PROPN
fcis-10203	193	9	,	,	PUNCT
fcis-10203	193	10	w.	w.	PROPN
fcis-10203	193	11	,	,	PUNCT
fcis-10203	193	12	fu	fu	PROPN
fcis-10203	193	13	,	,	PUNCT
fcis-10203	193	14	j.	j.	PROPN
fcis-10203	193	15	,	,	PUNCT
fcis-10203	193	16	jiang	jiang	PROPN
fcis-10203	193	17	,	,	PUNCT
fcis-10203	193	18	z.	z.	PROPN
fcis-10203	193	19	,	,	PUNCT
fcis-10203	193	20	hayashi	hayashi	PROPN
fcis-10203	193	21	,	,	PUNCT
fcis-10203	193	22	h.	h.	PROPN
fcis-10203	193	23	,	,	PUNCT
fcis-10203	193	24	&	&	CCONJ
fcis-10203	193	25	neubig	neubig	PROPN
fcis-10203	193	26	,	,	PUNCT
fcis-10203	193	27	g.	g.	PROPN
fcis-10203	193	28	(	(	PUNCT
fcis-10203	193	29	2021	2021	NUM
fcis-10203	193	30	)	)	PUNCT
fcis-10203	193	31	.	.	PUNCT
fcis-10203	194	1	pre	pre	ADJ
fcis-10203	194	2	-	-	ADJ
fcis-10203	194	3	train	train	ADJ
fcis-10203	194	4	,	,	PUNCT
fcis-10203	194	5	prompt	prompt	ADJ
fcis-10203	194	6	,	,	PUNCT
fcis-10203	194	7	and	and	CCONJ
fcis-10203	194	8	predict	predict	VERB
fcis-10203	194	9	:	:	PUNCT
fcis-10203	194	10	a	a	DET
fcis-10203	194	11	systematic	systematic	ADJ
fcis-10203	194	12	survey	survey	NOUN
fcis-10203	194	13	of	of	ADP
fcis-10203	194	14	prompting	prompt	VERB
fcis-10203	194	15	methods	method	NOUN
fcis-10203	194	16	in	in	ADP
fcis-10203	194	17	natural	natural	ADJ
fcis-10203	194	18	language	language	NOUN
fcis-10203	194	19	processing	processing	NOUN
fcis-10203	194	20	.	.	PUNCT
fcis-10203	195	1	arxiv	arxiv	PROPN
fcis-10203	195	2	preprint	preprint	NOUN
fcis-10203	195	3	arxiv:2107.13586	arxiv:2107.13586	NOUN
fcis-10203	195	4	.	.	PUNCT
fcis-10203	196	1	[	[	X
fcis-10203	196	2	13	13	NUM
fcis-10203	196	3	]	]	SYM
fcis-10203	196	4	khashabi	khashabi	NOUN
fcis-10203	196	5	,	,	PUNCT
fcis-10203	196	6	d.	d.	PROPN
fcis-10203	196	7	,	,	PUNCT
fcis-10203	196	8	min	min	PROPN
fcis-10203	196	9	,	,	PUNCT
fcis-10203	196	10	s.	s.	PROPN
fcis-10203	196	11	,	,	PUNCT
fcis-10203	196	12	khot	khot	ADJ
fcis-10203	196	13	,	,	PUNCT
fcis-10203	196	14	t.	t.	PROPN
fcis-10203	196	15	,	,	PUNCT
fcis-10203	196	16	sabharwal	sabharwal	PROPN
fcis-10203	196	17	,	,	PUNCT
fcis-10203	196	18	a.	a.	NOUN
fcis-10203	196	19	,	,	PUNCT
fcis-10203	196	20	tafjord	tafjord	NOUN
fcis-10203	196	21	,	,	PUNCT
fcis-10203	196	22	o.	o.	PROPN
fcis-10203	196	23	,	,	PUNCT
fcis-10203	196	24	clark	clark	PROPN
fcis-10203	196	25	,	,	PUNCT
fcis-10203	196	26	p.	p.	PROPN
fcis-10203	196	27	,	,	PUNCT
fcis-10203	196	28	&	&	CCONJ
fcis-10203	196	29	hajishirzi	hajishirzi	NOUN
fcis-10203	196	30	,	,	PUNCT
fcis-10203	196	31	h.	h.	PROPN
fcis-10203	196	32	(	(	PUNCT
fcis-10203	196	33	2020	2020	NUM
fcis-10203	196	34	)	)	PUNCT
fcis-10203	196	35	.	.	PUNCT
fcis-10203	197	1	unifiedqa	unifiedqa	NOUN
fcis-10203	197	2	:	:	PUNCT
fcis-10203	197	3	crossing	crossing	NOUN
fcis-10203	197	4	format	format	NOUN
fcis-10203	197	5	boundaries	boundary	NOUN
fcis-10203	197	6	with	with	ADP
fcis-10203	197	7	a	a	DET
fcis-10203	197	8	single	single	ADJ
fcis-10203	197	9	qa	qa	PROPN
fcis-10203	197	10	system	system	NOUN
fcis-10203	197	11	.	.	PUNCT
fcis-10203	198	1	arxiv	arxiv	PROPN
fcis-10203	198	2	preprint	preprint	PROPN
fcis-10203	198	3	arxiv	arxiv	PROPN
fcis-10203	198	4	:	:	PUNCT
fcis-10203	198	5	2005	2005	NUM
fcis-10203	198	6	.	.	PUNCT
fcis-10203	198	7	00700	00700	NUM
fcis-10203	198	8	.	.	PUNCT
fcis-10203	199	1	67	67	NUM
fcis-10203	200	1	[	[	X
fcis-10203	200	2	14	14	NUM
fcis-10203	200	3	]	]	X
fcis-10203	200	4	khashabi	khashabi	PROPN
fcis-10203	200	5	,	,	PUNCT
fcis-10203	200	6	d.	d.	PROPN
fcis-10203	200	7	,	,	PUNCT
fcis-10203	200	8	kordi	kordi	PROPN
fcis-10203	200	9	,	,	PUNCT
fcis-10203	200	10	y.	y.	PROPN
fcis-10203	200	11	,	,	PUNCT
fcis-10203	200	12	&	&	CCONJ
fcis-10203	200	13	hajishirzi	hajishirzi	NOUN
fcis-10203	200	14	,	,	PUNCT
fcis-10203	200	15	h.	h.	PROPN
fcis-10203	200	16	(	(	PUNCT
fcis-10203	200	17	2022	2022	NUM
fcis-10203	200	18	)	)	PUNCT
fcis-10203	200	19	.	.	PUNCT
fcis-10203	201	1	unifiedqa	unifiedqa	NOUN
fcis-10203	201	2	-	-	PUNCT
fcis-10203	201	3	v2	v2	NOUN
fcis-10203	201	4	:	:	PUNCT
fcis-10203	201	5	stronger	strong	ADJ
fcis-10203	201	6	generalization	generalization	NOUN
fcis-10203	201	7	via	via	ADP
fcis-10203	201	8	broader	broad	ADJ
fcis-10203	201	9	cross	cross	ADJ
fcis-10203	201	10	-	-	ADJ
fcis-10203	201	11	format	format	ADJ
fcis-10203	201	12	training	training	NOUN
fcis-10203	201	13	.	.	PUNCT
fcis-10203	202	1	arxiv	arxiv	PROPN
fcis-10203	202	2	preprint	preprint	VERB
fcis-10203	202	3	arxiv:2202.12359	arxiv:2202.12359	ADV
fcis-10203	202	4	.	.	PUNCT
fcis-10203	203	1	[	[	X
fcis-10203	203	2	15	15	NUM
fcis-10203	203	3	]	]	X
fcis-10203	203	4	budzianowski	budzianowski	NOUN
fcis-10203	203	5	,	,	PUNCT
fcis-10203	203	6	p.	p.	PROPN
fcis-10203	203	7	,	,	PUNCT
fcis-10203	203	8	&	&	CCONJ
fcis-10203	203	9	vulić	vulić	PROPN
fcis-10203	203	10	,	,	PUNCT
fcis-10203	203	11	i.	i.	PROPN
fcis-10203	203	12	(	(	PUNCT
fcis-10203	203	13	2019	2019	NUM
fcis-10203	203	14	)	)	PUNCT
fcis-10203	203	15	.	.	PUNCT
fcis-10203	204	1	hello	hello	INTJ
fcis-10203	204	2	,	,	PUNCT
fcis-10203	204	3	it	it	PRON
fcis-10203	204	4	's	be	AUX
fcis-10203	204	5	gpt-2	gpt-2	NOUN
fcis-10203	204	6	-	-	NUM
fcis-10203	204	7	-how	-how	X
fcis-10203	204	8	can	can	AUX
fcis-10203	204	9	i	i	PRON
fcis-10203	204	10	help	help	VERB
fcis-10203	204	11	you	you	PRON
fcis-10203	204	12	?	?	PUNCT
fcis-10203	205	1	towards	towards	ADP
fcis-10203	205	2	the	the	DET
fcis-10203	205	3	use	use	NOUN
fcis-10203	205	4	of	of	ADP
fcis-10203	205	5	pretrained	pretraine	VERB
fcis-10203	205	6	language	language	NOUN
fcis-10203	205	7	models	model	NOUN
fcis-10203	205	8	for	for	ADP
fcis-10203	205	9	task	task	NOUN
fcis-10203	205	10	-	-	PUNCT
fcis-10203	205	11	oriented	orient	VERB
fcis-10203	205	12	dialogue	dialogue	NOUN
fcis-10203	205	13	systems	system	NOUN
fcis-10203	205	14	.	.	PUNCT
fcis-10203	206	1	arxiv	arxiv	PROPN
fcis-10203	206	2	preprint	preprint	PROPN
fcis-10203	206	3	arxiv	arxiv	PROPN
fcis-10203	206	4	:	:	PUNCT
fcis-10203	206	5	1907	1907	NUM
fcis-10203	206	6	.	.	PUNCT
fcis-10203	206	7	05774	05774	NUM
fcis-10203	206	8	.	.	PUNCT
fcis-10203	207	1	[	[	X
fcis-10203	207	2	16	16	NUM
fcis-10203	207	3	]	]	X
fcis-10203	207	4	floridi	floridi	PROPN
fcis-10203	207	5	,	,	PUNCT
fcis-10203	207	6	l.	l.	PROPN
fcis-10203	207	7	,	,	PUNCT
fcis-10203	207	8	&	&	CCONJ
fcis-10203	207	9	chiriatti	chiriatti	PROPN
fcis-10203	207	10	,	,	PUNCT
fcis-10203	207	11	m.	m.	NOUN
fcis-10203	207	12	(	(	PUNCT
fcis-10203	207	13	2020	2020	NUM
fcis-10203	207	14	)	)	PUNCT
fcis-10203	207	15	.	.	PUNCT
fcis-10203	208	1	gpt-3	gpt-3	NOUN
fcis-10203	208	2	:	:	PUNCT
fcis-10203	208	3	its	its	PRON
fcis-10203	208	4	nature	nature	NOUN
fcis-10203	208	5	,	,	PUNCT
fcis-10203	208	6	scope	scope	NOUN
fcis-10203	208	7	,	,	PUNCT
fcis-10203	208	8	limits	limit	NOUN
fcis-10203	208	9	,	,	PUNCT
fcis-10203	208	10	and	and	CCONJ
fcis-10203	208	11	consequences	consequence	NOUN
fcis-10203	208	12	.	.	PUNCT
fcis-10203	209	1	minds	mind	NOUN
fcis-10203	209	2	and	and	CCONJ
fcis-10203	209	3	machines	machine	NOUN
fcis-10203	209	4	,	,	PUNCT
fcis-10203	209	5	30(4	30(4	NUM
fcis-10203	209	6	)	)	PUNCT
fcis-10203	209	7	,	,	PUNCT
fcis-10203	209	8	681	681	NUM
fcis-10203	209	9	-	-	SYM
fcis-10203	209	10	694	694	NUM
fcis-10203	209	11	.	.	PUNCT
fcis-10203	210	1	[	[	X
fcis-10203	210	2	17	17	NUM
fcis-10203	210	3	]	]	SYM
fcis-10203	210	4	ouyang	ouyang	PROPN
fcis-10203	210	5	,	,	PUNCT
fcis-10203	210	6	l.	l.	PROPN
fcis-10203	210	7	,	,	PUNCT
fcis-10203	210	8	wu	wu	PROPN
fcis-10203	210	9	,	,	PUNCT
fcis-10203	210	10	j.	j.	PROPN
fcis-10203	210	11	,	,	PUNCT
fcis-10203	210	12	jiang	jiang	PROPN
fcis-10203	210	13	,	,	PUNCT
fcis-10203	210	14	x.	x.	PROPN
fcis-10203	210	15	,	,	PUNCT
fcis-10203	210	16	almeida	almeida	PROPN
fcis-10203	210	17	,	,	PUNCT
fcis-10203	210	18	d.	d.	PROPN
fcis-10203	210	19	,	,	PUNCT
fcis-10203	210	20	wainwright	wainwright	PROPN
fcis-10203	210	21	,	,	PUNCT
fcis-10203	210	22	c.	c.	PROPN
fcis-10203	210	23	l.	l.	PROPN
fcis-10203	210	24	,	,	PUNCT
fcis-10203	210	25	mishkin	mishkin	PROPN
fcis-10203	210	26	,	,	PUNCT
fcis-10203	210	27	p.	p.	PROPN
fcis-10203	210	28	,	,	PUNCT
fcis-10203	210	29	...	...	PUNCT
fcis-10203	210	30	&	&	CCONJ
fcis-10203	210	31	lowe	lowe	PROPN
fcis-10203	210	32	,	,	PUNCT
fcis-10203	210	33	r.	r.	PROPN
fcis-10203	210	34	(	(	PUNCT
fcis-10203	210	35	2022	2022	NUM
fcis-10203	210	36	)	)	PUNCT
fcis-10203	210	37	.	.	PUNCT
fcis-10203	211	1	training	training	NOUN
fcis-10203	211	2	language	language	NOUN
fcis-10203	211	3	models	model	NOUN
fcis-10203	211	4	to	to	PART
fcis-10203	211	5	follow	follow	VERB
fcis-10203	211	6	instructions	instruction	NOUN
fcis-10203	211	7	with	with	ADP
fcis-10203	211	8	human	human	ADJ
fcis-10203	211	9	feedback	feedback	NOUN
fcis-10203	211	10	.	.	PUNCT
fcis-10203	212	1	arxiv	arxiv	PROPN
fcis-10203	212	2	preprint	preprint	PROPN
fcis-10203	212	3	arxiv	arxiv	PROPN
fcis-10203	212	4	:	:	PUNCT
fcis-10203	212	5	2203.02155	2203.02155	X
fcis-10203	212	6	.	.	PUNCT
fcis-10203	213	1	[	[	X
fcis-10203	213	2	18	18	NUM
fcis-10203	213	3	]	]	PUNCT
fcis-10203	213	4	openai	openai	NOUN
fcis-10203	213	5	.	.	PUNCT
fcis-10203	214	1	(	(	PUNCT
fcis-10203	214	2	2023	2023	NUM
fcis-10203	214	3	)	)	PUNCT
fcis-10203	214	4	.	.	PUNCT
fcis-10203	215	1	gpt-4	gpt-4	X
fcis-10203	215	2	technical	technical	ADJ
fcis-10203	215	3	report	report	NOUN
fcis-10203	215	4	.	.	PUNCT
fcis-10203	216	1	.https://	.https://	PROPN
fcis-10203	216	2	openai	openai	PROPN
fcis-10203	216	3	.	.	PUNCT
fcis-10203	217	1	com/	com/	NOUN
fcis-10203	217	2	product	product	NOUN
fcis-10203	217	3	/	/	PUNCT
fcis-10203	217	4	gpt-4	gpt-4	NOUN
fcis-10203	217	5	.	.	PUNCT
fcis-10203	218	1	[	[	X
fcis-10203	218	2	19	19	NUM
fcis-10203	218	3	]	]	X
fcis-10203	218	4	lester	lester	PROPN
fcis-10203	218	5	,	,	PUNCT
fcis-10203	218	6	b.	b.	PROPN
fcis-10203	218	7	,	,	PUNCT
fcis-10203	218	8	al	al	PROPN
fcis-10203	218	9	-	-	PUNCT
fcis-10203	218	10	rfou	rfou	PROPN
fcis-10203	218	11	,	,	PUNCT
fcis-10203	218	12	r.	r.	PROPN
fcis-10203	218	13	,	,	PUNCT
fcis-10203	218	14	&	&	CCONJ
fcis-10203	218	15	constant	constant	ADJ
fcis-10203	218	16	,	,	PUNCT
fcis-10203	218	17	n.	n.	NOUN
fcis-10203	218	18	(	(	PUNCT
fcis-10203	218	19	2021	2021	NUM
fcis-10203	218	20	)	)	PUNCT
fcis-10203	218	21	.	.	PUNCT
fcis-10203	219	1	the	the	DET
fcis-10203	219	2	power	power	NOUN
fcis-10203	219	3	of	of	ADP
fcis-10203	219	4	scale	scale	NOUN
fcis-10203	219	5	for	for	ADP
fcis-10203	219	6	parameter	parameter	NOUN
fcis-10203	219	7	-	-	PUNCT
fcis-10203	219	8	efficient	efficient	ADJ
fcis-10203	219	9	prompt	prompt	ADJ
fcis-10203	219	10	tuning	tuning	NOUN
fcis-10203	219	11	.	.	PUNCT
fcis-10203	220	1	arxiv	arxiv	PROPN
fcis-10203	220	2	preprint	preprint	NOUN
fcis-10203	220	3	arxiv:2104.08691	arxiv:2104.08691	NOUN
fcis-10203	220	4	.	.	PUNCT