id	sid	tid	token	lemma	pos
fcis-25717	1	1	frontiers	frontier	NOUN
fcis-25717	1	2	in	in	ADP
fcis-25717	1	3	computing	computing	NOUN
fcis-25717	1	4	and	and	CCONJ
fcis-25717	1	5	intelligent	intelligent	ADJ
fcis-25717	1	6	systems	system	NOUN
fcis-25717	1	7	issn	issn	VERB
fcis-25717	1	8	:	:	PUNCT
fcis-25717	1	9	2832	2832	NUM
fcis-25717	1	10	-	-	SYM
fcis-25717	1	11	6024	6024	NUM
fcis-25717	1	12	|	|	NOUN
fcis-25717	1	13	vol	vol	NOUN
fcis-25717	1	14	.	.	PROPN
fcis-25717	2	1	9	9	NUM
fcis-25717	2	2	,	,	PUNCT
fcis-25717	2	3	no	no	INTJ
fcis-25717	2	4	.	.	NOUN
fcis-25717	2	5	3	3	NUM
fcis-25717	2	6	,	,	PUNCT
fcis-25717	2	7	2024	2024	NUM
fcis-25717	2	8	40	40	NUM
fcis-25717	2	9	exploring	explore	VERB
fcis-25717	2	10	deep	deep	ADJ
fcis-25717	2	11	learning	learning	NOUN
fcis-25717	2	12	models	model	NOUN
fcis-25717	2	13	for	for	ADP
fcis-25717	2	14	lyric	lyric	ADJ
fcis-25717	2	15	generation	generation	NOUN
fcis-25717	2	16	and	and	CCONJ
fcis-25717	2	17	addressing	address	VERB
fcis-25717	2	18	biases	bias	NOUN
fcis-25717	2	19	in	in	ADP
fcis-25717	2	20	word	word	NOUN
fcis-25717	2	21	embeddings	embedding	NOUN
fcis-25717	2	22	lijie	lijie	PROPN
fcis-25717	2	23	liu	liu	PROPN
fcis-25717	2	24	rensselaer	rensselaer	PROPN
fcis-25717	2	25	polytechnic	polytechnic	PROPN
fcis-25717	2	26	institute	institute	PROPN
fcis-25717	2	27	troy	troy	PROPN
fcis-25717	2	28	,	,	PUNCT
fcis-25717	2	29	troy	troy	PROPN
fcis-25717	2	30	,	,	PUNCT
fcis-25717	2	31	new	new	PROPN
fcis-25717	2	32	york	york	PROPN
fcis-25717	2	33	,	,	PUNCT
fcis-25717	2	34	12180	12180	NUM
fcis-25717	2	35	usa	usa	PROPN
fcis-25717	2	36	abstract	abstract	NOUN
fcis-25717	2	37	:	:	PUNCT
fcis-25717	2	38	the	the	DET
fcis-25717	2	39	aim	aim	NOUN
fcis-25717	2	40	of	of	ADP
fcis-25717	2	41	this	this	DET
fcis-25717	2	42	project	project	NOUN
fcis-25717	2	43	is	be	AUX
fcis-25717	2	44	to	to	PART
fcis-25717	2	45	explore	explore	VERB
fcis-25717	2	46	the	the	DET
fcis-25717	2	47	performance	performance	NOUN
fcis-25717	2	48	of	of	ADP
fcis-25717	2	49	different	different	ADJ
fcis-25717	2	50	model	model	NOUN
fcis-25717	2	51	architectures	architecture	NOUN
fcis-25717	2	52	(	(	PUNCT
fcis-25717	2	53	such	such	ADJ
fcis-25717	2	54	as	as	ADP
fcis-25717	2	55	rnn	rnn	PROPN
fcis-25717	2	56	,	,	PUNCT
fcis-25717	2	57	lstm	lstm	PROPN
fcis-25717	2	58	,	,	PUNCT
fcis-25717	2	59	gru	gru	PROPN
fcis-25717	2	60	)	)	PUNCT
fcis-25717	2	61	by	by	ADP
fcis-25717	2	62	generating	generate	VERB
fcis-25717	2	63	lyrics	lyric	NOUN
fcis-25717	2	64	using	use	VERB
fcis-25717	2	65	deep	deep	ADJ
fcis-25717	2	66	learning	learning	NOUN
fcis-25717	2	67	models	model	NOUN
fcis-25717	2	68	,	,	PUNCT
fcis-25717	2	69	and	and	CCONJ
fcis-25717	2	70	to	to	PART
fcis-25717	2	71	use	use	VERB
fcis-25717	2	72	the	the	DET
fcis-25717	2	73	word2vec	word2vec	PROPN
fcis-25717	2	74	model	model	NOUN
fcis-25717	2	75	for	for	ADP
fcis-25717	2	76	distributed	distribute	VERB
fcis-25717	2	77	semantic	semantic	ADJ
fcis-25717	2	78	analysis	analysis	NOUN
fcis-25717	2	79	to	to	PART
fcis-25717	2	80	understand	understand	VERB
fcis-25717	2	81	semantic	semantic	ADJ
fcis-25717	2	82	phenomena	phenomenon	NOUN
fcis-25717	2	83	and	and	CCONJ
fcis-25717	2	84	potential	potential	ADJ
fcis-25717	2	85	biases	bias	NOUN
fcis-25717	2	86	in	in	ADP
fcis-25717	2	87	word	word	NOUN
fcis-25717	2	88	embedding	embed	VERB
fcis-25717	2	89	models	model	NOUN
fcis-25717	2	90	.	.	PUNCT
fcis-25717	3	1	the	the	DET
fcis-25717	3	2	experimental	experimental	ADJ
fcis-25717	3	3	results	result	NOUN
fcis-25717	3	4	show	show	VERB
fcis-25717	3	5	that	that	SCONJ
fcis-25717	3	6	lstm	lstm	NOUN
fcis-25717	3	7	and	and	CCONJ
fcis-25717	3	8	gru	gru	NOUN
fcis-25717	3	9	perform	perform	VERB
fcis-25717	3	10	better	well	ADV
fcis-25717	3	11	than	than	ADP
fcis-25717	3	12	traditional	traditional	ADJ
fcis-25717	3	13	rnn	rnn	NOUN
fcis-25717	3	14	models	model	NOUN
fcis-25717	3	15	when	when	SCONJ
fcis-25717	3	16	processing	process	VERB
fcis-25717	3	17	long	long	ADJ
fcis-25717	3	18	sequence	sequence	NOUN
fcis-25717	3	19	data	datum	NOUN
fcis-25717	3	20	.	.	PUNCT
fcis-25717	4	1	in	in	ADP
fcis-25717	4	2	addition	addition	NOUN
fcis-25717	4	3	,	,	PUNCT
fcis-25717	4	4	by	by	ADP
fcis-25717	4	5	analyzing	analyze	VERB
fcis-25717	4	6	word	word	NOUN
fcis-25717	4	7	embeddings	embedding	NOUN
fcis-25717	4	8	,	,	PUNCT
fcis-25717	4	9	we	we	PRON
fcis-25717	4	10	revealed	reveal	VERB
fcis-25717	4	11	potential	potential	ADJ
fcis-25717	4	12	gender	gender	NOUN
fcis-25717	4	13	and	and	CCONJ
fcis-25717	4	14	racial	racial	ADJ
fcis-25717	4	15	biases	bias	NOUN
fcis-25717	4	16	and	and	CCONJ
fcis-25717	4	17	proposed	propose	VERB
fcis-25717	4	18	corresponding	correspond	VERB
fcis-25717	4	19	solutions	solution	NOUN
fcis-25717	4	20	.	.	PUNCT
fcis-25717	5	1	keywords	keyword	NOUN
fcis-25717	5	2	:	:	PUNCT
fcis-25717	5	3	lyrics	lyric	NOUN
fcis-25717	5	4	generation	generation	NOUN
fcis-25717	5	5	;	;	PUNCT
fcis-25717	5	6	deep	deep	ADJ
fcis-25717	5	7	learning	learning	NOUN
fcis-25717	5	8	models	model	NOUN
fcis-25717	5	9	;	;	PUNCT
fcis-25717	5	10	biases	bias	NOUN
fcis-25717	5	11	in	in	ADP
fcis-25717	5	12	word	word	NOUN
fcis-25717	5	13	embeddings	embedding	NOUN
fcis-25717	5	14	.	.	PUNCT
fcis-25717	6	1	1	1	X
fcis-25717	6	2	.	.	X
fcis-25717	6	3	introduction	introduction	NOUN
fcis-25717	6	4	lyrics	lyric	NOUN
fcis-25717	6	5	generation	generation	NOUN
fcis-25717	6	6	is	be	AUX
fcis-25717	6	7	an	an	DET
fcis-25717	6	8	important	important	ADJ
fcis-25717	6	9	application	application	NOUN
fcis-25717	6	10	in	in	ADP
fcis-25717	6	11	natural	natural	ADJ
fcis-25717	6	12	language	language	NOUN
fcis-25717	6	13	processing	processing	NOUN
fcis-25717	6	14	,	,	PUNCT
fcis-25717	6	15	which	which	PRON
fcis-25717	6	16	can	can	AUX
fcis-25717	6	17	be	be	AUX
fcis-25717	6	18	used	use	VERB
fcis-25717	6	19	not	not	PART
fcis-25717	6	20	only	only	ADV
fcis-25717	6	21	for	for	ADP
fcis-25717	6	22	music	music	NOUN
fcis-25717	6	23	creation	creation	NOUN
fcis-25717	6	24	,	,	PUNCT
fcis-25717	6	25	but	but	CCONJ
fcis-25717	6	26	also	also	ADV
fcis-25717	6	27	for	for	ADP
fcis-25717	6	28	automatic	automatic	ADJ
fcis-25717	6	29	dialogue	dialogue	NOUN
fcis-25717	6	30	systems	system	NOUN
fcis-25717	6	31	,	,	PUNCT
fcis-25717	6	32	text	text	NOUN
fcis-25717	6	33	summarization	summarization	NOUN
fcis-25717	6	34	,	,	PUNCT
fcis-25717	6	35	and	and	CCONJ
fcis-25717	6	36	other	other	ADJ
fcis-25717	6	37	fields	field	NOUN
fcis-25717	6	38	.	.	PUNCT
fcis-25717	7	1	traditional	traditional	ADJ
fcis-25717	7	2	recurrent	recurrent	ADJ
fcis-25717	7	3	neural	neural	ADJ
fcis-25717	7	4	networks	network	NOUN
fcis-25717	7	5	(	(	PUNCT
fcis-25717	7	6	rnns	rnns	PROPN
fcis-25717	7	7	)	)	PUNCT
fcis-25717	7	8	have	have	VERB
fcis-25717	7	9	unique	unique	ADJ
fcis-25717	7	10	advantages	advantage	NOUN
fcis-25717	7	11	in	in	ADP
fcis-25717	7	12	processing	process	VERB
fcis-25717	7	13	sequential	sequential	ADJ
fcis-25717	7	14	data	datum	NOUN
fcis-25717	7	15	,	,	PUNCT
fcis-25717	7	16	but	but	CCONJ
fcis-25717	7	17	there	there	PRON
fcis-25717	7	18	is	be	VERB
fcis-25717	7	19	a	a	DET
fcis-25717	7	20	gradient	gradient	ADJ
fcis-25717	7	21	vanishing	vanishing	NOUN
fcis-25717	7	22	problem	problem	NOUN
fcis-25717	7	23	when	when	SCONJ
fcis-25717	7	24	dealing	deal	VERB
fcis-25717	7	25	with	with	ADP
fcis-25717	7	26	long	long	ADJ
fcis-25717	7	27	sequence	sequence	NOUN
fcis-25717	7	28	data	datum	NOUN
fcis-25717	7	29	.	.	PUNCT
fcis-25717	8	1	to	to	PART
fcis-25717	8	2	address	address	VERB
fcis-25717	8	3	this	this	DET
fcis-25717	8	4	issue	issue	NOUN
fcis-25717	8	5	,	,	PUNCT
fcis-25717	8	6	this	this	DET
fcis-25717	8	7	study	study	NOUN
fcis-25717	8	8	introduced	introduce	VERB
fcis-25717	8	9	long	long	ADJ
fcis-25717	8	10	short	short	ADJ
fcis-25717	8	11	-	-	PUNCT
fcis-25717	8	12	term	term	NOUN
fcis-25717	8	13	memory	memory	NOUN
fcis-25717	8	14	(	(	PUNCT
fcis-25717	8	15	lstm	lstm	NOUN
fcis-25717	8	16	)	)	PUNCT
fcis-25717	8	17	and	and	CCONJ
fcis-25717	8	18	gated	gate	VERB
fcis-25717	8	19	recurrent	recurrent	ADJ
fcis-25717	8	20	unit	unit	NOUN
fcis-25717	8	21	(	(	PUNCT
fcis-25717	8	22	gru	gru	PROPN
fcis-25717	8	23	)	)	PUNCT
fcis-25717	8	24	and	and	CCONJ
fcis-25717	8	25	compared	compare	VERB
fcis-25717	8	26	their	their	PRON
fcis-25717	8	27	performance	performance	NOUN
fcis-25717	8	28	in	in	ADP
fcis-25717	8	29	lyric	lyric	ADJ
fcis-25717	8	30	generation	generation	NOUN
fcis-25717	8	31	tasks	task	NOUN
fcis-25717	8	32	.	.	PUNCT
fcis-25717	9	1	in	in	ADP
fcis-25717	9	2	addition	addition	NOUN
fcis-25717	9	3	,	,	PUNCT
fcis-25717	9	4	we	we	PRON
fcis-25717	9	5	also	also	ADV
fcis-25717	9	6	use	use	VERB
fcis-25717	9	7	the	the	DET
fcis-25717	9	8	word2vec	word2vec	PROPN
fcis-25717	9	9	model	model	NOUN
fcis-25717	9	10	for	for	ADP
fcis-25717	9	11	semantic	semantic	ADJ
fcis-25717	9	12	analysis	analysis	NOUN
fcis-25717	9	13	of	of	ADP
fcis-25717	9	14	the	the	DET
fcis-25717	9	15	generated	generate	VERB
fcis-25717	9	16	word	word	NOUN
fcis-25717	9	17	embeddings	embedding	NOUN
fcis-25717	9	18	to	to	PART
fcis-25717	9	19	reveal	reveal	VERB
fcis-25717	9	20	potential	potential	ADJ
fcis-25717	9	21	biases	bias	NOUN
fcis-25717	9	22	in	in	ADP
fcis-25717	9	23	the	the	DET
fcis-25717	9	24	model	model	NOUN
fcis-25717	9	25	and	and	CCONJ
fcis-25717	9	26	propose	propose	VERB
fcis-25717	9	27	solutions	solution	NOUN
fcis-25717	9	28	.	.	PUNCT
fcis-25717	10	1	“	"	PUNCT
fcis-25717	10	2	deep	deep	ADJ
fcis-25717	10	3	learning	learning	NOUN
fcis-25717	10	4	models	model	NOUN
fcis-25717	10	5	such	such	ADJ
fcis-25717	10	6	as	as	ADP
fcis-25717	10	7	rnns	rnn	NOUN
fcis-25717	10	8	,	,	PUNCT
fcis-25717	10	9	lstms	lstms	ADJ
fcis-25717	10	10	,	,	PUNCT
fcis-25717	10	11	and	and	CCONJ
fcis-25717	10	12	grus	grus	NOUN
fcis-25717	10	13	are	be	AUX
fcis-25717	10	14	pivotal	pivotal	ADJ
fcis-25717	10	15	in	in	ADP
fcis-25717	10	16	processing	process	VERB
fcis-25717	10	17	sequential	sequential	ADJ
fcis-25717	10	18	data	datum	NOUN
fcis-25717	10	19	,	,	PUNCT
fcis-25717	10	20	particularly	particularly	ADV
fcis-25717	10	21	in	in	ADP
fcis-25717	10	22	tasks	task	NOUN
fcis-25717	10	23	involving	involve	VERB
fcis-25717	10	24	language	language	NOUN
fcis-25717	10	25	generation	generation	NOUN
fcis-25717	10	26	”	"	PUNCT
fcis-25717	10	27	[	[	X
fcis-25717	10	28	1	1	NUM
fcis-25717	10	29	]	]	PUNCT
fcis-25717	10	30	.	.	PUNCT
fcis-25717	11	1	however	however	ADV
fcis-25717	11	2	,	,	PUNCT
fcis-25717	11	3	while	while	SCONJ
fcis-25717	11	4	rnns	rnn	NOUN
fcis-25717	11	5	are	be	AUX
fcis-25717	11	6	foundational	foundational	ADJ
fcis-25717	11	7	in	in	ADP
fcis-25717	11	8	this	this	DET
fcis-25717	11	9	field	field	NOUN
fcis-25717	11	10	,	,	PUNCT
fcis-25717	11	11	they	they	PRON
fcis-25717	11	12	are	be	AUX
fcis-25717	11	13	limited	limit	VERB
fcis-25717	11	14	by	by	ADP
fcis-25717	11	15	their	their	PRON
fcis-25717	11	16	inability	inability	NOUN
fcis-25717	11	17	to	to	PART
fcis-25717	11	18	effectively	effectively	ADV
fcis-25717	11	19	manage	manage	VERB
fcis-25717	11	20	long	long	ADJ
fcis-25717	11	21	-	-	PUNCT
fcis-25717	11	22	term	term	NOUN
fcis-25717	11	23	dependencies	dependency	NOUN
fcis-25717	11	24	,	,	PUNCT
fcis-25717	11	25	a	a	DET
fcis-25717	11	26	challenge	challenge	NOUN
fcis-25717	11	27	that	that	PRON
fcis-25717	11	28	lstms	lstms	NOUN
fcis-25717	11	29	and	and	CCONJ
fcis-25717	11	30	grus	grus	NOUN
fcis-25717	11	31	are	be	AUX
fcis-25717	11	32	specifically	specifically	ADV
fcis-25717	11	33	designed	design	VERB
fcis-25717	11	34	to	to	PART
fcis-25717	11	35	address	address	VERB
fcis-25717	11	36	.	.	PUNCT
fcis-25717	12	1	2	2	X
fcis-25717	12	2	.	.	X
fcis-25717	12	3	data	datum	NOUN
fcis-25717	12	4	and	and	CCONJ
fcis-25717	12	5	methods	method	NOUN
fcis-25717	12	6	2.1	2.1	NUM
fcis-25717	12	7	.	.	PUNCT
fcis-25717	13	1	data	datum	NOUN
fcis-25717	13	2	preparation	preparation	NOUN
fcis-25717	13	3	we	we	PRON
fcis-25717	13	4	used	use	VERB
fcis-25717	13	5	a	a	DET
fcis-25717	13	6	text	text	NOUN
fcis-25717	13	7	dataset	dataset	NOUN
fcis-25717	13	8	containing	contain	VERB
fcis-25717	13	9	a	a	DET
fcis-25717	13	10	large	large	ADJ
fcis-25717	13	11	number	number	NOUN
fcis-25717	13	12	of	of	ADP
fcis-25717	13	13	lyrics	lyric	NOUN
fcis-25717	13	14	.	.	PUNCT
fcis-25717	14	1	during	during	ADP
fcis-25717	14	2	the	the	DET
fcis-25717	14	3	data	data	NOUN
fcis-25717	14	4	preparation	preparation	NOUN
fcis-25717	14	5	process	process	NOUN
fcis-25717	14	6	,	,	PUNCT
fcis-25717	14	7	we	we	PRON
fcis-25717	14	8	carried	carry	VERB
fcis-25717	14	9	out	out	ADP
fcis-25717	14	10	the	the	DET
fcis-25717	14	11	following	follow	VERB
fcis-25717	14	12	steps	step	NOUN
fcis-25717	14	13	:	:	PUNCT
fcis-25717	15	1			X
fcis-25717	15	2	data	datum	NOUN
fcis-25717	15	3	cleaning	clean	VERB
fcis-25717	15	4	:	:	PUNCT
fcis-25717	15	5	irrelevant	irrelevant	ADJ
fcis-25717	15	6	characters	character	NOUN
fcis-25717	15	7	and	and	CCONJ
fcis-25717	15	8	punctuation	punctuation	NOUN
fcis-25717	15	9	have	have	AUX
fcis-25717	15	10	been	be	AUX
fcis-25717	15	11	removed	remove	VERB
fcis-25717	15	12	,	,	PUNCT
fcis-25717	15	13	and	and	CCONJ
fcis-25717	15	14	text	text	NOUN
fcis-25717	15	15	formatting	formatting	NOUN
fcis-25717	15	16	has	have	AUX
fcis-25717	15	17	been	be	AUX
fcis-25717	15	18	standardized	standardize	VERB
fcis-25717	15	19	.	.	PUNCT
fcis-25717	16	1			PRON
fcis-25717	16	2	word	word	NOUN
fcis-25717	16	3	segmentation	segmentation	NOUN
fcis-25717	16	4	and	and	CCONJ
fcis-25717	16	5	encoding	encoding	NOUN
fcis-25717	16	6	:	:	PUNCT
fcis-25717	16	7	divide	divide	VERB
fcis-25717	16	8	lyrics	lyric	NOUN
fcis-25717	16	9	into	into	ADP
fcis-25717	16	10	word	word	NOUN
fcis-25717	16	11	or	or	CCONJ
fcis-25717	16	12	character	character	NOUN
fcis-25717	16	13	sequences	sequence	NOUN
fcis-25717	16	14	and	and	CCONJ
fcis-25717	16	15	use	use	VERB
fcis-25717	16	16	one	one	NUM
fcis-25717	16	17	hot	hot	ADJ
fcis-25717	16	18	encoding	encoding	NOUN
fcis-25717	16	19	or	or	CCONJ
fcis-25717	16	20	embedding	embed	VERB
fcis-25717	16	21	vectors	vector	NOUN
fcis-25717	16	22	to	to	PART
fcis-25717	16	23	represent	represent	VERB
fcis-25717	16	24	them	they	PRON
fcis-25717	16	25	.	.	PUNCT
fcis-25717	17	1	below	below	ADV
fcis-25717	17	2	are	be	AUX
fcis-25717	17	3	the	the	DET
fcis-25717	17	4	outputs	output	NOUN
fcis-25717	17	5	:	:	PUNCT
fcis-25717	17	6	2.2	2.2	NUM
fcis-25717	17	7	.	.	PUNCT
fcis-25717	17	8	model	model	NOUN
fcis-25717	17	9	construction	construction	NOUN
fcis-25717	17	10	and	and	CCONJ
fcis-25717	17	11	training	training	NOUN
fcis-25717	17	12	2.2.1	2.2.1	NUM
fcis-25717	17	13	.	.	PUNCT
fcis-25717	18	1	preliminary	preliminary	ADJ
fcis-25717	18	2	training	training	NOUN
fcis-25717	18	3	using	use	VERB
fcis-25717	18	4	vanilla	vanilla	NOUN
fcis-25717	18	5	rnn	rnn	NOUN
fcis-25717	18	6	we	we	PRON
fcis-25717	18	7	first	first	ADV
fcis-25717	18	8	constructed	construct	VERB
fcis-25717	18	9	a	a	DET
fcis-25717	18	10	basic	basic	ADJ
fcis-25717	18	11	vanilla	vanilla	NOUN
fcis-25717	18	12	rnn	rnn	NOUN
fcis-25717	18	13	model	model	NOUN
fcis-25717	18	14	,	,	PUNCT
fcis-25717	18	15	including	include	VERB
fcis-25717	18	16	an	an	DET
fcis-25717	18	17	input	input	NOUN
fcis-25717	18	18	layer	layer	NOUN
fcis-25717	18	19	,	,	PUNCT
fcis-25717	18	20	rnn	rnn	VERB
fcis-25717	18	21	layer	layer	NOUN
fcis-25717	18	22	,	,	PUNCT
fcis-25717	18	23	and	and	CCONJ
fcis-25717	18	24	output	output	NOUN
fcis-25717	18	25	layer	layer	NOUN
fcis-25717	18	26	,	,	PUNCT
fcis-25717	18	27	and	and	CCONJ
fcis-25717	18	28	implemented	implement	VERB
fcis-25717	18	29	forward	forward	ADP
fcis-25717	18	30	propagation	propagation	NOUN
fcis-25717	18	31	,	,	PUNCT
fcis-25717	18	32	loss	loss	NOUN
fcis-25717	18	33	calculation	calculation	NOUN
fcis-25717	18	34	,	,	PUNCT
fcis-25717	18	35	and	and	CCONJ
fcis-25717	18	36	parameter	parameter	NOUN
fcis-25717	18	37	updates	update	NOUN
fcis-25717	18	38	using	use	VERB
fcis-25717	18	39	the	the	DET
fcis-25717	18	40	pytorch	pytorch	NOUN
fcis-25717	18	41	framework	framework	NOUN
fcis-25717	18	42	.	.	PUNCT
fcis-25717	19	1	2.2.2	2.2.2	X
fcis-25717	19	2	.	.	X
fcis-25717	19	3	replace	replace	VERB
fcis-25717	19	4	with	with	ADP
fcis-25717	19	5	lstm	lstm	NOUN
fcis-25717	19	6	or	or	CCONJ
fcis-25717	19	7	gru	gru	VERB
fcis-25717	19	8	and	and	CCONJ
fcis-25717	19	9	compare	compare	VERB
fcis-25717	19	10	to	to	PART
fcis-25717	19	11	compare	compare	VERB
fcis-25717	19	12	the	the	DET
fcis-25717	19	13	advantages	advantage	NOUN
fcis-25717	19	14	of	of	ADP
fcis-25717	19	15	lstm	lstm	NOUN
fcis-25717	19	16	and	and	CCONJ
fcis-25717	19	17	gru	gru	NOUN
fcis-25717	19	18	in	in	ADP
fcis-25717	19	19	processing	process	VERB
fcis-25717	19	20	long	long	ADJ
fcis-25717	19	21	sequence	sequence	NOUN
fcis-25717	19	22	data	datum	NOUN
fcis-25717	19	23	,	,	PUNCT
fcis-25717	19	24	we	we	PRON
fcis-25717	19	25	replaced	replace	VERB
fcis-25717	19	26	the	the	DET
fcis-25717	19	27	rnn	rnn	NOUN
fcis-25717	19	28	layer	layer	NOUN
fcis-25717	19	29	with	with	ADP
fcis-25717	19	30	either	either	CCONJ
fcis-25717	19	31	lstm	lstm	NOUN
fcis-25717	19	32	or	or	CCONJ
fcis-25717	19	33	gru	gru	NOUN
fcis-25717	19	34	layer	layer	NOUN
fcis-25717	19	35	and	and	CCONJ
fcis-25717	19	36	trained	train	VERB
fcis-25717	19	37	on	on	ADP
fcis-25717	19	38	the	the	DET
fcis-25717	19	39	same	same	ADJ
fcis-25717	19	40	dataset	dataset	NOUN
fcis-25717	19	41	.	.	PUNCT
fcis-25717	20	1	record	record	VERB
fcis-25717	20	2	the	the	DET
fcis-25717	20	3	loss	loss	NOUN
fcis-25717	20	4	value	value	NOUN
fcis-25717	20	5	for	for	ADP
fcis-25717	20	6	each	each	DET
fcis-25717	20	7	epoch	epoch	NOUN
fcis-25717	20	8	to	to	PART
fcis-25717	20	9	evaluate	evaluate	VERB
fcis-25717	20	10	the	the	DET
fcis-25717	20	11	convergence	convergence	NOUN
fcis-25717	20	12	speed	speed	NOUN
fcis-25717	20	13	and	and	CCONJ
fcis-25717	20	14	generation	generation	NOUN
fcis-25717	20	15	performance	performance	NOUN
fcis-25717	20	16	of	of	ADP
fcis-25717	20	17	the	the	DET
fcis-25717	20	18	model	model	NOUN
fcis-25717	20	19	.	.	PUNCT
fcis-25717	21	1	“	"	PUNCT
fcis-25717	21	2	lstm	lstm	PROPN
fcis-25717	21	3	and	and	CCONJ
fcis-25717	21	4	gru	gru	NOUN
fcis-25717	21	5	architectures	architecture	NOUN
fcis-25717	21	6	have	have	AUX
fcis-25717	21	7	been	be	AUX
fcis-25717	21	8	demonstrated	demonstrate	VERB
fcis-25717	21	9	to	to	PART
fcis-25717	21	10	effectively	effectively	ADV
fcis-25717	21	11	manage	manage	VERB
fcis-25717	21	12	long	long	ADJ
fcis-25717	21	13	-	-	PUNCT
fcis-25717	21	14	term	term	NOUN
fcis-25717	21	15	dependencies	dependency	NOUN
fcis-25717	21	16	,	,	PUNCT
fcis-25717	21	17	outperforming	outperform	VERB
fcis-25717	21	18	traditional	traditional	ADJ
fcis-25717	21	19	rnns	rnn	NOUN
fcis-25717	21	20	in	in	ADP
fcis-25717	21	21	various	various	ADJ
fcis-25717	21	22	language	language	NOUN
fcis-25717	21	23	processing	processing	NOUN
fcis-25717	21	24	tasks	task	NOUN
fcis-25717	21	25	”	"	PUNCT
fcis-25717	22	1	[	[	X
fcis-25717	22	2	2	2	NUM
fcis-25717	22	3	]	]	PUNCT
fcis-25717	22	4	.	.	PUNCT
fcis-25717	23	1	2.3	2.3	NUM
fcis-25717	23	2	.	.	PUNCT
fcis-25717	23	3	text	text	NOUN
fcis-25717	23	4	generation	generation	NOUN
fcis-25717	23	5	strategy	strategy	NOUN
fcis-25717	23	6	2.3.1	2.3.1	NUM
fcis-25717	23	7	.	.	PUNCT
fcis-25717	24	1	greedy	greedy	ADJ
fcis-25717	24	2	decoding	decode	VERB
fcis-25717	24	3	we	we	PRON
fcis-25717	24	4	have	have	AUX
fcis-25717	24	5	implemented	implement	VERB
fcis-25717	24	6	a	a	DET
fcis-25717	24	7	simple	simple	ADJ
fcis-25717	24	8	decoding	decoding	NOUN
fcis-25717	24	9	strategy	strategy	NOUN
fcis-25717	24	10	that	that	PRON
fcis-25717	24	11	generates	generate	VERB
fcis-25717	24	12	text	text	NOUN
fcis-25717	24	13	by	by	ADP
fcis-25717	24	14	selecting	select	VERB
fcis-25717	24	15	the	the	DET
fcis-25717	24	16	most	most	ADV
fcis-25717	24	17	likely	likely	ADJ
fcis-25717	24	18	next	next	ADJ
fcis-25717	24	19	word	word	NOUN
fcis-25717	24	20	at	at	ADP
fcis-25717	24	21	each	each	DET
fcis-25717	24	22	step	step	NOUN
fcis-25717	24	23	.	.	PUNCT
fcis-25717	25	1	2.3.2	2.3.2	X
fcis-25717	25	2	.	.	X
fcis-25717	25	3	sampling	sample	VERB
fcis-25717	25	4	decoding	decode	VERB
fcis-25717	25	5	we	we	PRON
fcis-25717	25	6	also	also	ADV
fcis-25717	25	7	implemented	implement	VERB
fcis-25717	25	8	sampling	sample	VERB
fcis-25717	25	9	decoding	decoding	NOUN
fcis-25717	25	10	,	,	PUNCT
fcis-25717	25	11	generating	generate	VERB
fcis-25717	25	12	diverse	diverse	ADJ
fcis-25717	25	13	texts	text	NOUN
fcis-25717	25	14	through	through	ADP
fcis-25717	25	15	random	random	ADJ
fcis-25717	25	16	sampling	sampling	NOUN
fcis-25717	25	17	,	,	PUNCT
fcis-25717	25	18	and	and	CCONJ
fcis-25717	25	19	controlling	control	VERB
fcis-25717	25	20	the	the	DET
fcis-25717	25	21	randomness	randomness	NOUN
fcis-25717	25	22	of	of	ADP
fcis-25717	25	23	sampling	sample	VERB
fcis-25717	25	24	by	by	ADP
fcis-25717	25	25	adjusting	adjust	VERB
fcis-25717	25	26	temperature	temperature	NOUN
fcis-25717	25	27	parameters	parameter	NOUN
fcis-25717	25	28	.	.	PUNCT
fcis-25717	26	1	2.4	2.4	NUM
fcis-25717	26	2	.	.	PUNCT
fcis-25717	27	1	distributed	distribute	VERB
fcis-25717	27	2	semantic	semantic	ADJ
fcis-25717	27	3	analysis	analysis	NOUN
fcis-25717	27	4	2.4.1	2.4.1	NUM
fcis-25717	27	5	.	.	PUNCT
fcis-25717	28	1	use	use	VERB
fcis-25717	28	2	word2vec	word2vec	PROPN
fcis-25717	28	3	model	model	NOUN
fcis-25717	28	4	for	for	ADP
fcis-25717	28	5	word	word	NOUN
fcis-25717	28	6	embedding	embed	VERB
fcis-25717	28	7	analysis	analysis	NOUN
fcis-25717	28	8	we	we	PRON
fcis-25717	28	9	loaded	load	VERB
fcis-25717	28	10	the	the	DET
fcis-25717	28	11	pre	pre	ADJ
fcis-25717	28	12	-	-	ADJ
fcis-25717	28	13	trained	train	VERB
fcis-25717	28	14	word2vec	word2vec	PROPN
fcis-25717	28	15	model	model	NOUN
fcis-25717	28	16	and	and	CCONJ
fcis-25717	28	17	vectorized	vectorize	VERB
fcis-25717	28	18	the	the	DET
fcis-25717	28	19	selected	select	VERB
fcis-25717	28	20	words	word	NOUN
fcis-25717	28	21	.	.	PUNCT
fcis-25717	29	1	then	then	ADV
fcis-25717	29	2	,	,	PUNCT
fcis-25717	29	3	the	the	DET
fcis-25717	29	4	word	word	NOUN
fcis-25717	29	5	embeddings	embedding	NOUN
fcis-25717	29	6	are	be	AUX
fcis-25717	29	7	reduced	reduce	VERB
fcis-25717	29	8	to	to	ADP
fcis-25717	29	9	a	a	DET
fcis-25717	29	10	two	two	NUM
fcis-25717	29	11	-	-	PUNCT
fcis-25717	29	12	dimensional	dimensional	ADJ
fcis-25717	29	13	space	space	NOUN
fcis-25717	29	14	for	for	ADP
fcis-25717	29	15	visualization	visualization	NOUN
fcis-25717	29	16	using	use	VERB
fcis-25717	29	17	pca	pca	PROPN
fcis-25717	29	18	or	or	CCONJ
fcis-25717	29	19	t	t	PROPN
fcis-25717	29	20	-	-	PUNCT
fcis-25717	29	21	sne	sne	NOUN
fcis-25717	29	22	.	.	PUNCT
fcis-25717	30	1	2.4.2	2.4.2	NUM
fcis-25717	30	2	.	.	PUNCT
fcis-25717	31	1	analysis	analysis	NOUN
fcis-25717	31	2	of	of	ADP
fcis-25717	31	3	synonyms	synonym	NOUN
fcis-25717	31	4	and	and	CCONJ
fcis-25717	31	5	antonyms	antonyms	PROPN
fcis-25717	31	6	calculate	calculate	VERB
fcis-25717	31	7	the	the	DET
fcis-25717	31	8	cosine	cosine	NOUN
fcis-25717	31	9	distance	distance	NOUN
fcis-25717	31	10	between	between	ADP
fcis-25717	31	11	given	give	VERB
fcis-25717	31	12	word	word	NOUN
fcis-25717	31	13	pairs	pair	NOUN
fcis-25717	31	14	to	to	PART
fcis-25717	31	15	reveal	reveal	VERB
fcis-25717	31	16	semantic	semantic	ADJ
fcis-25717	31	17	relationships	relationship	NOUN
fcis-25717	31	18	and	and	CCONJ
fcis-25717	31	19	potential	potential	ADJ
fcis-25717	31	20	biases	bias	NOUN
fcis-25717	31	21	between	between	ADP
fcis-25717	31	22	words	word	NOUN
fcis-25717	31	23	.	.	PUNCT
fcis-25717	32	1	2.5	2.5	NUM
fcis-25717	32	2	.	.	PUNCT
fcis-25717	33	1	dealing	deal	VERB
fcis-25717	33	2	with	with	ADP
fcis-25717	33	3	bias	bias	NOUN
fcis-25717	33	4	2.5.1	2.5.1	NUM
fcis-25717	33	5	.	.	PUNCT
fcis-25717	34	1	identify	identify	VERB
fcis-25717	34	2	and	and	CCONJ
fcis-25717	34	3	address	address	VERB
fcis-25717	34	4	gender	gender	NOUN
fcis-25717	34	5	or	or	CCONJ
fcis-25717	34	6	racial	racial	ADJ
fcis-25717	34	7	biases	bias	NOUN
fcis-25717	34	8	in	in	ADP
fcis-25717	34	9	word	word	NOUN
fcis-25717	34	10	embeddings	embedding	NOUN
fcis-25717	34	11	we	we	PRON
fcis-25717	34	12	identified	identify	VERB
fcis-25717	34	13	gender	gender	NOUN
fcis-25717	34	14	and	and	CCONJ
fcis-25717	34	15	racial	racial	ADJ
fcis-25717	34	16	biases	bias	NOUN
fcis-25717	34	17	in	in	ADP
fcis-25717	34	18	the	the	DET
fcis-25717	34	19	word	word	NOUN
fcis-25717	34	20	embedding	embed	VERB
fcis-25717	34	21	model	model	NOUN
fcis-25717	34	22	and	and	CCONJ
fcis-25717	34	23	proposed	propose	VERB
fcis-25717	34	24	the	the	DET
fcis-25717	34	25	following	follow	VERB
fcis-25717	34	26	solutions	solution	NOUN
fcis-25717	34	27	:	:	PUNCT
fcis-25717	34	28			NOUN
fcis-25717	34	29	dataset	dataset	ADJ
fcis-25717	34	30	balance	balance	NOUN
fcis-25717	34	31	:	:	PUNCT
fcis-25717	34	32	ensure	ensure	VERB
fcis-25717	34	33	a	a	DET
fcis-25717	34	34	balanced	balanced	ADJ
fcis-25717	34	35	number	number	NOUN
fcis-25717	34	36	of	of	ADP
fcis-25717	34	37	samples	sample	NOUN
fcis-25717	34	38	of	of	ADP
fcis-25717	34	39	different	different	ADJ
fcis-25717	34	40	genders	gender	NOUN
fcis-25717	34	41	and	and	CCONJ
fcis-25717	34	42	races	race	NOUN
fcis-25717	34	43	in	in	ADP
fcis-25717	34	44	the	the	DET
fcis-25717	34	45	training	training	NOUN
fcis-25717	34	46	dataset	dataset	NOUN
fcis-25717	34	47	.	.	PUNCT
fcis-25717	35	1	41	41	NUM
fcis-25717	35	2			NOUN
fcis-25717	35	3	regularization	regularization	NOUN
fcis-25717	35	4	method	method	NOUN
fcis-25717	35	5	:	:	PUNCT
fcis-25717	35	6	use	use	VERB
fcis-25717	35	7	adversarial	adversarial	ADJ
fcis-25717	35	8	training	training	NOUN
fcis-25717	35	9	and	and	CCONJ
fcis-25717	35	10	regularization	regularization	NOUN
fcis-25717	35	11	methods	method	NOUN
fcis-25717	35	12	during	during	ADP
fcis-25717	35	13	model	model	NOUN
fcis-25717	35	14	training	training	NOUN
fcis-25717	35	15	.	.	PUNCT
fcis-25717	36	1			X
fcis-25717	36	2	post	post	ADJ
fcis-25717	36	3	-	-	ADJ
fcis-25717	36	4	processing	processing	ADJ
fcis-25717	36	5	technique	technique	NOUN
fcis-25717	36	6	:	:	PUNCT
fcis-25717	36	7	use	use	VERB
fcis-25717	36	8	a	a	DET
fcis-25717	36	9	de	de	ADJ
fcis-25717	36	10	-	-	NOUN
fcis-25717	36	11	bias	bias	NOUN
fcis-25717	36	12	algorithm	algorithm	NOUN
fcis-25717	36	13	to	to	PART
fcis-25717	36	14	adjust	adjust	VERB
fcis-25717	36	15	word	word	NOUN
fcis-25717	36	16	embeddings	embedding	NOUN
fcis-25717	36	17	after	after	ADP
fcis-25717	36	18	model	model	NOUN
fcis-25717	36	19	training	training	NOUN
fcis-25717	36	20	.	.	PUNCT
fcis-25717	37	1	figure	figure	NOUN
fcis-25717	37	2	1	1	NUM
fcis-25717	37	3	.	.	PUNCT
fcis-25717	38	1	the	the	DET
fcis-25717	38	2	text	text	NOUN
fcis-25717	38	3	dataset	dataset	NOUN
fcis-25717	38	4	's	's	PART
fcis-25717	38	5	outputs	output	NOUN
fcis-25717	38	6	of	of	ADP
fcis-25717	38	7	experiment	experiment	NOUN
fcis-25717	38	8	“	"	PUNCT
fcis-25717	38	9	research	research	NOUN
fcis-25717	38	10	has	have	AUX
fcis-25717	38	11	shown	show	VERB
fcis-25717	38	12	that	that	SCONJ
fcis-25717	38	13	nlp	nlp	NOUN
fcis-25717	38	14	models	model	NOUN
fcis-25717	38	15	can	can	AUX
fcis-25717	38	16	inherit	inherit	VERB
fcis-25717	38	17	and	and	CCONJ
fcis-25717	38	18	even	even	ADV
fcis-25717	38	19	amplify	amplify	VERB
fcis-25717	38	20	biases	bias	NOUN
fcis-25717	38	21	present	present	ADJ
fcis-25717	38	22	in	in	ADP
fcis-25717	38	23	the	the	DET
fcis-25717	38	24	training	training	NOUN
fcis-25717	38	25	data	datum	NOUN
fcis-25717	38	26	,	,	PUNCT
fcis-25717	38	27	leading	lead	VERB
fcis-25717	38	28	to	to	ADP
fcis-25717	38	29	concerns	concern	NOUN
fcis-25717	38	30	over	over	ADP
fcis-25717	38	31	fairness	fairness	NOUN
fcis-25717	38	32	in	in	ADP
fcis-25717	38	33	ai	ai	PROPN
fcis-25717	38	34	applications	application	NOUN
fcis-25717	38	35	”	"	PUNCT
fcis-25717	39	1	[	[	X
fcis-25717	39	2	3	3	NUM
fcis-25717	39	3	]	]	PUNCT
fcis-25717	39	4	.	.	PUNCT
fcis-25717	40	1	3	3	X
fcis-25717	40	2	.	.	NOUN
fcis-25717	40	3	results	result	NOUN
fcis-25717	40	4	and	and	CCONJ
fcis-25717	40	5	discussion	discussion	NOUN
fcis-25717	40	6	through	through	ADP
fcis-25717	40	7	training	training	NOUN
fcis-25717	40	8	and	and	CCONJ
fcis-25717	40	9	comparing	compare	VERB
fcis-25717	40	10	vanilla	vanilla	NOUN
fcis-25717	40	11	rnn	rnn	NOUN
fcis-25717	40	12	,	,	PUNCT
fcis-25717	40	13	lstm	lstm	NOUN
fcis-25717	40	14	,	,	PUNCT
fcis-25717	40	15	and	and	CCONJ
fcis-25717	40	16	gru	gru	NOUN
fcis-25717	40	17	models	model	NOUN
fcis-25717	40	18	,	,	PUNCT
fcis-25717	40	19	we	we	PRON
fcis-25717	40	20	found	find	VERB
fcis-25717	40	21	that	that	SCONJ
fcis-25717	40	22	lstm	lstm	NOUN
fcis-25717	40	23	and	and	CCONJ
fcis-25717	40	24	gru	gru	NOUN
fcis-25717	40	25	exhibit	exhibit	VERB
fcis-25717	40	26	better	well	ADJ
fcis-25717	40	27	performance	performance	NOUN
fcis-25717	40	28	in	in	ADP
fcis-25717	40	29	processing	process	VERB
fcis-25717	40	30	long	long	ADJ
fcis-25717	40	31	sequence	sequence	NOUN
fcis-25717	40	32	data	datum	NOUN
fcis-25717	40	33	,	,	PUNCT
fcis-25717	40	34	specifically	specifically	ADV
fcis-25717	40	35	in	in	ADP
fcis-25717	40	36	terms	term	NOUN
fcis-25717	40	37	of	of	ADP
fcis-25717	40	38	faster	fast	ADJ
fcis-25717	40	39	convergence	convergence	NOUN
fcis-25717	40	40	speed	speed	NOUN
fcis-25717	40	41	and	and	CCONJ
fcis-25717	40	42	higher	high	ADJ
fcis-25717	40	43	quality	quality	NOUN
fcis-25717	40	44	generated	generate	VERB
fcis-25717	40	45	lyrics	lyric	NOUN
fcis-25717	40	46	text	text	NOUN
fcis-25717	40	47	.	.	PUNCT
fcis-25717	41	1	through	through	ADP
fcis-25717	41	2	distributed	distribute	VERB
fcis-25717	41	3	semantic	semantic	ADJ
fcis-25717	41	4	analysis	analysis	NOUN
fcis-25717	41	5	,	,	PUNCT
fcis-25717	41	6	we	we	PRON
fcis-25717	41	7	have	have	AUX
fcis-25717	41	8	revealed	reveal	VERB
fcis-25717	41	9	the	the	DET
fcis-25717	41	10	semantic	semantic	ADJ
fcis-25717	41	11	relationships	relationship	NOUN
fcis-25717	41	12	and	and	CCONJ
fcis-25717	41	13	potential	potential	ADJ
fcis-25717	41	14	biases	bias	NOUN
fcis-25717	41	15	between	between	ADP
fcis-25717	41	16	words	word	NOUN
fcis-25717	41	17	.	.	PUNCT
fcis-25717	42	1	we	we	PRON
fcis-25717	42	2	calculated	calculate	VERB
fcis-25717	42	3	the	the	DET
fcis-25717	42	4	cosine	cosine	NOUN
fcis-25717	42	5	distance	distance	NOUN
fcis-25717	42	6	between	between	ADP
fcis-25717	42	7	given	give	VERB
fcis-25717	42	8	word	word	NOUN
fcis-25717	42	9	pairs	pair	NOUN
fcis-25717	42	10	,	,	PUNCT
fcis-25717	42	11	with	with	ADP
fcis-25717	42	12	a	a	DET
fcis-25717	42	13	particular	particular	ADJ
fcis-25717	42	14	focus	focus	NOUN
fcis-25717	42	15	on	on	ADP
fcis-25717	42	16	the	the	DET
fcis-25717	42	17	distance	distance	NOUN
fcis-25717	42	18	between	between	ADP
fcis-25717	42	19	synonyms	synonym	NOUN
fcis-25717	42	20	and	and	CCONJ
fcis-25717	42	21	antonyms	antonym	NOUN
fcis-25717	42	22	.	.	PUNCT
fcis-25717	43	1	when	when	SCONJ
fcis-25717	43	2	the	the	DET
fcis-25717	43	3	distance	distance	NOUN
fcis-25717	43	4	between	between	ADP
fcis-25717	43	5	synonyms	synonym	NOUN
fcis-25717	43	6	and	and	CCONJ
fcis-25717	43	7	antonyms	antonyms	PROPN
fcis-25717	43	8	does	do	AUX
fcis-25717	43	9	not	not	PART
fcis-25717	43	10	conform	conform	VERB
fcis-25717	43	11	to	to	ADP
fcis-25717	43	12	intuition	intuition	NOUN
fcis-25717	43	13	,	,	PUNCT
fcis-25717	43	14	we	we	PRON
fcis-25717	43	15	delved	delve	VERB
fcis-25717	43	16	into	into	ADP
fcis-25717	43	17	possible	possible	ADJ
fcis-25717	43	18	semantic	semantic	ADJ
fcis-25717	43	19	biases	bias	NOUN
fcis-25717	43	20	and	and	CCONJ
fcis-25717	43	21	explained	explain	VERB
fcis-25717	43	22	the	the	DET
fcis-25717	43	23	results	result	NOUN
fcis-25717	43	24	.	.	PUNCT
fcis-25717	44	1	this	this	DET
fcis-25717	44	2	analysis	analysis	NOUN
fcis-25717	44	3	helps	help	VERB
fcis-25717	44	4	to	to	PART
fcis-25717	44	5	reveal	reveal	VERB
fcis-25717	44	6	the	the	DET
fcis-25717	44	7	ability	ability	NOUN
fcis-25717	44	8	of	of	ADP
fcis-25717	44	9	word	word	NOUN
fcis-25717	44	10	embedding	embed	VERB
fcis-25717	44	11	models	model	NOUN
fcis-25717	44	12	in	in	ADP
fcis-25717	44	13	capturing	capture	VERB
fcis-25717	44	14	semantic	semantic	ADJ
fcis-25717	44	15	relationships	relationship	NOUN
fcis-25717	44	16	,	,	PUNCT
fcis-25717	44	17	as	as	ADV
fcis-25717	44	18	well	well	ADV
fcis-25717	44	19	as	as	ADP
fcis-25717	44	20	potential	potential	ADJ
fcis-25717	44	21	biases	bias	NOUN
fcis-25717	44	22	in	in	ADP
fcis-25717	44	23	the	the	DET
fcis-25717	44	24	models	model	NOUN
fcis-25717	44	25	.	.	PUNCT
fcis-25717	45	1	additionally	additionally	ADV
fcis-25717	45	2	,	,	PUNCT
fcis-25717	45	3	the	the	DET
fcis-25717	45	4	results	result	NOUN
fcis-25717	45	5	of	of	ADP
fcis-25717	45	6	our	our	PRON
fcis-25717	45	7	study	study	NOUN
fcis-25717	45	8	showed	show	VERB
fcis-25717	45	9	that	that	SCONJ
fcis-25717	45	10	lstm	lstm	NOUN
fcis-25717	45	11	and	and	CCONJ
fcis-25717	45	12	gru	gru	NOUN
fcis-25717	45	13	models	model	NOUN
fcis-25717	45	14	converge	converge	VERB
fcis-25717	45	15	faster	fast	ADV
fcis-25717	45	16	than	than	SCONJ
fcis-25717	45	17	vanilla	vanilla	NOUN
fcis-25717	45	18	rnn	rnn	VERB
fcis-25717	45	19	due	due	ADP
fcis-25717	45	20	to	to	ADP
fcis-25717	45	21	their	their	PRON
fcis-25717	45	22	ability	ability	NOUN
fcis-25717	45	23	to	to	PART
fcis-25717	45	24	effectively	effectively	ADV
fcis-25717	45	25	handle	handle	VERB
fcis-25717	45	26	long	long	ADJ
fcis-25717	45	27	-	-	PUNCT
fcis-25717	45	28	term	term	NOUN
fcis-25717	45	29	dependencies	dependency	NOUN
fcis-25717	45	30	.	.	PUNCT
fcis-25717	46	1	for	for	ADP
fcis-25717	46	2	instance	instance	NOUN
fcis-25717	46	3	,	,	PUNCT
fcis-25717	46	4	we	we	PRON
fcis-25717	46	5	trained	train	VERB
fcis-25717	46	6	the	the	DET
fcis-25717	46	7	models	model	NOUN
fcis-25717	46	8	using	use	VERB
fcis-25717	46	9	a	a	DET
fcis-25717	46	10	large	large	ADJ
fcis-25717	46	11	lyric	lyric	ADJ
fcis-25717	46	12	dataset	dataset	NOUN
fcis-25717	46	13	and	and	CCONJ
fcis-25717	46	14	found	find	VERB
fcis-25717	46	15	that	that	SCONJ
fcis-25717	46	16	the	the	DET
fcis-25717	46	17	loss	loss	NOUN
fcis-25717	46	18	values	value	NOUN
fcis-25717	46	19	decreased	decrease	VERB
fcis-25717	46	20	more	more	ADV
fcis-25717	46	21	rapidly	rapidly	ADV
fcis-25717	46	22	for	for	ADP
fcis-25717	46	23	lstm	lstm	NOUN
fcis-25717	46	24	and	and	CCONJ
fcis-25717	46	25	gru	gru	NOUN
fcis-25717	46	26	compared	compare	VERB
fcis-25717	46	27	to	to	ADP
fcis-25717	46	28	vanilla	vanilla	NOUN
fcis-25717	46	29	rnn	rnn	NOUN
fcis-25717	46	30	.	.	PUNCT
fcis-25717	47	1	this	this	PRON
fcis-25717	47	2	indicates	indicate	VERB
fcis-25717	47	3	a	a	DET
fcis-25717	47	4	better	well	ADJ
fcis-25717	47	5	learning	learning	NOUN
fcis-25717	47	6	process	process	NOUN
fcis-25717	47	7	and	and	CCONJ
fcis-25717	47	8	improved	improve	VERB
fcis-25717	47	9	text	text	NOUN
fcis-25717	47	10	coherence	coherence	NOUN
fcis-25717	47	11	in	in	ADP
fcis-25717	47	12	the	the	DET
fcis-25717	47	13	generated	generate	VERB
fcis-25717	47	14	lyrics	lyric	NOUN
fcis-25717	47	15	.	.	PUNCT
fcis-25717	48	1	by	by	ADP
fcis-25717	48	2	implementing	implement	VERB
fcis-25717	48	3	a	a	DET
fcis-25717	48	4	pad_sequence	pad_sequence	NOUN
fcis-25717	48	5	function	function	NOUN
fcis-25717	48	6	,	,	PUNCT
fcis-25717	48	7	we	we	PRON
fcis-25717	48	8	ensured	ensure	VERB
fcis-25717	48	9	that	that	SCONJ
fcis-25717	48	10	all	all	DET
fcis-25717	48	11	input	input	NOUN
fcis-25717	48	12	data	datum	NOUN
fcis-25717	48	13	had	have	VERB
fcis-25717	48	14	the	the	DET
fcis-25717	48	15	same	same	ADJ
fcis-25717	48	16	length	length	NOUN
fcis-25717	48	17	,	,	PUNCT
fcis-25717	48	18	which	which	PRON
fcis-25717	48	19	is	be	AUX
fcis-25717	48	20	crucial	crucial	ADJ
fcis-25717	48	21	for	for	ADP
fcis-25717	48	22	training	train	VERB
fcis-25717	48	23	neural	neural	ADJ
fcis-25717	48	24	networks	network	NOUN
fcis-25717	48	25	,	,	PUNCT
fcis-25717	48	26	especially	especially	ADV
fcis-25717	48	27	those	those	PRON
fcis-25717	48	28	involving	involve	VERB
fcis-25717	48	29	sequence	sequence	NOUN
fcis-25717	48	30	processing	processing	NOUN
fcis-25717	48	31	.	.	PUNCT
fcis-25717	49	1	this	this	DET
fcis-25717	49	2	method	method	NOUN
fcis-25717	49	3	helps	help	VERB
fcis-25717	49	4	in	in	ADP
fcis-25717	49	5	retaining	retain	VERB
fcis-25717	49	6	as	as	ADV
fcis-25717	49	7	much	much	ADJ
fcis-25717	49	8	information	information	NOUN
fcis-25717	49	9	as	as	ADP
fcis-25717	49	10	possible	possible	ADJ
fcis-25717	49	11	by	by	ADP
fcis-25717	49	12	padding	padding	NOUN
fcis-25717	49	13	shorter	short	ADJ
fcis-25717	49	14	texts	text	NOUN
fcis-25717	49	15	instead	instead	ADV
fcis-25717	49	16	of	of	ADP
fcis-25717	49	17	truncating	truncate	VERB
fcis-25717	49	18	them	they	PRON
fcis-25717	49	19	,	,	PUNCT
fcis-25717	49	20	thus	thus	ADV
fcis-25717	49	21	preserving	preserve	VERB
fcis-25717	49	22	the	the	DET
fcis-25717	49	23	important	important	ADJ
fcis-25717	49	24	information	information	NOUN
fcis-25717	49	25	at	at	ADP
fcis-25717	49	26	the	the	DET
fcis-25717	49	27	beginning	beginning	NOUN
fcis-25717	49	28	and	and	CCONJ
fcis-25717	49	29	end	end	NOUN
fcis-25717	49	30	of	of	ADP
fcis-25717	49	31	the	the	DET
fcis-25717	49	32	sequences	sequence	NOUN
fcis-25717	49	33	.	.	PUNCT
fcis-25717	50	1	in	in	ADP
fcis-25717	50	2	the	the	DET
fcis-25717	50	3	semantic	semantic	ADJ
fcis-25717	50	4	analysis	analysis	NOUN
fcis-25717	50	5	using	use	VERB
fcis-25717	50	6	the	the	DET
fcis-25717	50	7	word2vec	word2vec	PROPN
fcis-25717	50	8	model	model	NOUN
fcis-25717	50	9	,	,	PUNCT
fcis-25717	50	10	we	we	PRON
fcis-25717	50	11	loaded	load	VERB
fcis-25717	50	12	pre	pre	ADJ
fcis-25717	50	13	-	-	ADJ
fcis-25717	50	14	trained	train	VERB
fcis-25717	50	15	embeddings	embedding	NOUN
fcis-25717	50	16	and	and	CCONJ
fcis-25717	50	17	visualized	visualize	VERB
fcis-25717	50	18	the	the	DET
fcis-25717	50	19	word	word	NOUN
fcis-25717	50	20	vectors	vector	NOUN
fcis-25717	50	21	in	in	ADP
fcis-25717	50	22	a	a	DET
fcis-25717	50	23	two	two	NUM
fcis-25717	50	24	-	-	PUNCT
fcis-25717	50	25	dimensional	dimensional	ADJ
fcis-25717	50	26	space	space	NOUN
fcis-25717	50	27	using	use	VERB
fcis-25717	50	28	pca	pca	PROPN
fcis-25717	50	29	and	and	CCONJ
fcis-25717	50	30	t	t	PROPN
fcis-25717	50	31	-	-	PUNCT
fcis-25717	50	32	sne	sne	NOUN
fcis-25717	50	33	.	.	PUNCT
fcis-25717	51	1	this	this	DET
fcis-25717	51	2	visualization	visualization	NOUN
fcis-25717	51	3	revealed	reveal	VERB
fcis-25717	51	4	clusters	cluster	NOUN
fcis-25717	51	5	of	of	ADP
fcis-25717	51	6	semantically	semantically	ADV
fcis-25717	51	7	related	relate	VERB
fcis-25717	51	8	42	42	NUM
fcis-25717	51	9	words	word	NOUN
fcis-25717	51	10	and	and	CCONJ
fcis-25717	51	11	helped	help	VERB
fcis-25717	51	12	us	we	PRON
fcis-25717	51	13	identify	identify	VERB
fcis-25717	51	14	potential	potential	ADJ
fcis-25717	51	15	gender	gender	NOUN
fcis-25717	51	16	and	and	CCONJ
fcis-25717	51	17	racial	racial	ADJ
fcis-25717	51	18	biases	bias	NOUN
fcis-25717	51	19	.	.	PUNCT
fcis-25717	52	1	for	for	ADP
fcis-25717	52	2	example	example	NOUN
fcis-25717	52	3	,	,	PUNCT
fcis-25717	52	4	we	we	PRON
fcis-25717	52	5	observed	observe	VERB
fcis-25717	52	6	that	that	SCONJ
fcis-25717	52	7	words	word	NOUN
fcis-25717	52	8	related	relate	VERB
fcis-25717	52	9	to	to	ADP
fcis-25717	52	10	"	"	PUNCT
fcis-25717	52	11	man	man	NOUN
fcis-25717	52	12	"	"	PUNCT
fcis-25717	52	13	and	and	CCONJ
fcis-25717	52	14	"	"	PUNCT
fcis-25717	52	15	woman	woman	NOUN
fcis-25717	52	16	"	"	PUNCT
fcis-25717	52	17	showed	show	VERB
fcis-25717	52	18	significant	significant	ADJ
fcis-25717	52	19	differences	difference	NOUN
fcis-25717	52	20	in	in	ADP
fcis-25717	52	21	their	their	PRON
fcis-25717	52	22	association	association	NOUN
fcis-25717	52	23	with	with	ADP
fcis-25717	52	24	certain	certain	ADJ
fcis-25717	52	25	professions	profession	NOUN
fcis-25717	52	26	,	,	PUNCT
fcis-25717	52	27	indicating	indicate	VERB
fcis-25717	52	28	a	a	DET
fcis-25717	52	29	bias	bias	NOUN
fcis-25717	52	30	in	in	ADP
fcis-25717	52	31	the	the	DET
fcis-25717	52	32	word	word	NOUN
fcis-25717	52	33	embeddings	embedding	NOUN
fcis-25717	52	34	.	.	PUNCT
fcis-25717	53	1	to	to	PART
fcis-25717	53	2	address	address	VERB
fcis-25717	53	3	these	these	DET
fcis-25717	53	4	biases	bias	NOUN
fcis-25717	53	5	,	,	PUNCT
fcis-25717	53	6	we	we	PRON
fcis-25717	53	7	proposed	propose	VERB
fcis-25717	53	8	several	several	ADJ
fcis-25717	53	9	methods	method	NOUN
fcis-25717	53	10	,	,	PUNCT
fcis-25717	53	11	including	include	VERB
fcis-25717	53	12	balancing	balance	VERB
fcis-25717	53	13	the	the	DET
fcis-25717	53	14	dataset	dataset	NOUN
fcis-25717	53	15	to	to	PART
fcis-25717	53	16	ensure	ensure	VERB
fcis-25717	53	17	an	an	DET
fcis-25717	53	18	equal	equal	ADJ
fcis-25717	53	19	representation	representation	NOUN
fcis-25717	53	20	of	of	ADP
fcis-25717	53	21	different	different	ADJ
fcis-25717	53	22	genders	gender	NOUN
fcis-25717	53	23	and	and	CCONJ
fcis-25717	53	24	races	race	NOUN
fcis-25717	53	25	,	,	PUNCT
fcis-25717	53	26	using	use	VERB
fcis-25717	53	27	adversarial	adversarial	ADJ
fcis-25717	53	28	training	training	NOUN
fcis-25717	53	29	and	and	CCONJ
fcis-25717	53	30	regularization	regularization	NOUN
fcis-25717	53	31	methods	method	NOUN
fcis-25717	53	32	to	to	PART
fcis-25717	53	33	mitigate	mitigate	VERB
fcis-25717	53	34	bias	bias	NOUN
fcis-25717	53	35	during	during	ADP
fcis-25717	53	36	model	model	NOUN
fcis-25717	53	37	training	training	NOUN
fcis-25717	53	38	,	,	PUNCT
fcis-25717	53	39	and	and	CCONJ
fcis-25717	53	40	applying	apply	VERB
fcis-25717	53	41	post	post	ADJ
fcis-25717	53	42	-	-	ADJ
fcis-25717	53	43	processing	processing	ADJ
fcis-25717	53	44	techniques	technique	NOUN
fcis-25717	53	45	such	such	ADJ
fcis-25717	53	46	as	as	ADP
fcis-25717	53	47	de	de	NOUN
fcis-25717	53	48	-	-	ADJ
fcis-25717	53	49	biasing	biasing	ADJ
fcis-25717	53	50	algorithms	algorithm	NOUN
fcis-25717	53	51	to	to	PART
fcis-25717	53	52	adjust	adjust	VERB
fcis-25717	53	53	the	the	DET
fcis-25717	53	54	word	word	NOUN
fcis-25717	53	55	embeddings	embedding	NOUN
fcis-25717	53	56	after	after	ADP
fcis-25717	53	57	training	train	VERB
fcis-25717	53	58	[	[	X
fcis-25717	53	59	4	4	NUM
fcis-25717	53	60	]	]	PUNCT
fcis-25717	53	61	.	.	PUNCT
fcis-25717	54	1	4	4	X
fcis-25717	54	2	.	.	X
fcis-25717	54	3	conclusion	conclusion	NOUN
fcis-25717	54	4	this	this	DET
fcis-25717	54	5	project	project	NOUN
fcis-25717	54	6	demonstrates	demonstrate	VERB
fcis-25717	54	7	the	the	DET
fcis-25717	54	8	application	application	NOUN
fcis-25717	54	9	of	of	ADP
fcis-25717	54	10	deep	deep	ADJ
fcis-25717	54	11	learning	learning	NOUN
fcis-25717	54	12	models	model	NOUN
fcis-25717	54	13	in	in	ADP
fcis-25717	54	14	lyric	lyric	ADJ
fcis-25717	54	15	generation	generation	NOUN
fcis-25717	54	16	tasks	task	NOUN
fcis-25717	54	17	,	,	PUNCT
fcis-25717	54	18	and	and	CCONJ
fcis-25717	54	19	selects	select	VERB
fcis-25717	54	20	the	the	DET
fcis-25717	54	21	most	most	ADV
fcis-25717	54	22	suitable	suitable	ADJ
fcis-25717	54	23	model	model	NOUN
fcis-25717	54	24	architecture	architecture	NOUN
fcis-25717	54	25	by	by	ADP
fcis-25717	54	26	comparing	compare	VERB
fcis-25717	54	27	the	the	DET
fcis-25717	54	28	performance	performance	NOUN
fcis-25717	54	29	of	of	ADP
fcis-25717	54	30	different	different	ADJ
fcis-25717	54	31	models	model	NOUN
fcis-25717	54	32	.	.	PUNCT
fcis-25717	55	1	through	through	ADP
fcis-25717	55	2	semantic	semantic	ADJ
fcis-25717	55	3	analysis	analysis	NOUN
fcis-25717	55	4	of	of	ADP
fcis-25717	55	5	the	the	DET
fcis-25717	55	6	word2vec	word2vec	ADJ
fcis-25717	55	7	word	word	NOUN
fcis-25717	55	8	embedding	embed	VERB
fcis-25717	55	9	model	model	NOUN
fcis-25717	55	10	,	,	PUNCT
fcis-25717	55	11	we	we	PRON
fcis-25717	55	12	identified	identify	VERB
fcis-25717	55	13	and	and	CCONJ
fcis-25717	55	14	discussed	discuss	VERB
fcis-25717	55	15	potential	potential	ADJ
fcis-25717	55	16	biases	bias	NOUN
fcis-25717	55	17	in	in	ADP
fcis-25717	55	18	the	the	DET
fcis-25717	55	19	model	model	NOUN
fcis-25717	55	20	,	,	PUNCT
fcis-25717	55	21	and	and	CCONJ
fcis-25717	55	22	proposed	propose	VERB
fcis-25717	55	23	corresponding	correspond	VERB
fcis-25717	55	24	solutions	solution	NOUN
fcis-25717	55	25	.	.	PUNCT
fcis-25717	56	1	these	these	DET
fcis-25717	56	2	research	research	NOUN
fcis-25717	56	3	findings	finding	NOUN
fcis-25717	56	4	provide	provide	VERB
fcis-25717	56	5	valuable	valuable	ADJ
fcis-25717	56	6	insights	insight	NOUN
fcis-25717	56	7	for	for	ADP
fcis-25717	56	8	further	far	ADV
fcis-25717	56	9	improving	improve	VERB
fcis-25717	56	10	lyric	lyric	ADJ
fcis-25717	56	11	generation	generation	NOUN
fcis-25717	56	12	models	model	NOUN
fcis-25717	56	13	and	and	CCONJ
fcis-25717	56	14	offer	offer	VERB
fcis-25717	56	15	methods	method	NOUN
fcis-25717	56	16	for	for	ADP
fcis-25717	56	17	addressing	address	VERB
fcis-25717	56	18	biases	bias	NOUN
fcis-25717	56	19	in	in	ADP
fcis-25717	56	20	training	training	NOUN
fcis-25717	56	21	data	datum	NOUN
fcis-25717	56	22	.	.	PUNCT
fcis-25717	57	1	by	by	ADP
fcis-25717	57	2	implementing	implement	VERB
fcis-25717	57	3	these	these	DET
fcis-25717	57	4	strategies	strategy	NOUN
fcis-25717	57	5	,	,	PUNCT
fcis-25717	57	6	we	we	PRON
fcis-25717	57	7	can	can	AUX
fcis-25717	57	8	ensure	ensure	VERB
fcis-25717	57	9	that	that	SCONJ
fcis-25717	57	10	the	the	DET
fcis-25717	57	11	models	model	NOUN
fcis-25717	57	12	generate	generate	VERB
fcis-25717	57	13	more	more	ADV
fcis-25717	57	14	coherent	coherent	ADJ
fcis-25717	57	15	and	and	CCONJ
fcis-25717	57	16	fair	fair	ADJ
fcis-25717	57	17	lyrics	lyric	NOUN
fcis-25717	57	18	,	,	PUNCT
fcis-25717	57	19	contributing	contribute	VERB
fcis-25717	57	20	to	to	ADP
fcis-25717	57	21	the	the	DET
fcis-25717	57	22	development	development	NOUN
fcis-25717	57	23	of	of	ADP
fcis-25717	57	24	advanced	advanced	ADJ
fcis-25717	57	25	natural	natural	ADJ
fcis-25717	57	26	language	language	NOUN
fcis-25717	57	27	processing	processing	NOUN
fcis-25717	57	28	applications	application	NOUN
fcis-25717	57	29	.	.	PUNCT
fcis-25717	58	1	references	reference	NOUN
fcis-25717	58	2	[	[	X
fcis-25717	58	3	1	1	NUM
fcis-25717	58	4	]	]	X
fcis-25717	58	5	smith	smith	PROPN
fcis-25717	58	6	,	,	PUNCT
fcis-25717	58	7	john	john	PROPN
fcis-25717	58	8	,	,	PUNCT
fcis-25717	58	9	et	et	PROPN
fcis-25717	58	10	al	al	PROPN
fcis-25717	58	11	.	.	PUNCT
fcis-25717	59	1	"	"	PUNCT
fcis-25717	59	2	introduction	introduction	NOUN
fcis-25717	59	3	to	to	PART
fcis-25717	59	4	recurrent	recurrent	VERB
fcis-25717	59	5	neural	neural	ADJ
fcis-25717	59	6	networks	network	NOUN
fcis-25717	59	7	.	.	PUNCT
fcis-25717	59	8	"	"	PUNCT
fcis-25717	60	1	stanford	stanford	PROPN
fcis-25717	60	2	university	university	PROPN
fcis-25717	60	3	,	,	PUNCT
fcis-25717	60	4	2023	2023	NUM
fcis-25717	60	5	,	,	PUNCT
fcis-25717	60	6	www	www	PROPN
fcis-25717	60	7	.	.	PROPN
fcis-25717	60	8	stanford.edu/	stanford.edu/	VERB
fcis-25717	60	9	research/	research/	NUM
fcis-25717	60	10	recurrent	recurrent	ADJ
fcis-25717	60	11	-	-	PUNCT
fcis-25717	60	12	neural	neural	ADJ
fcis-25717	60	13	-	-	PUNCT
fcis-25717	60	14	networks	network	NOUN
fcis-25717	60	15	.	.	PUNCT
fcis-25717	61	1	[	[	X
fcis-25717	61	2	2	2	NUM
fcis-25717	61	3	]	]	X
fcis-25717	61	4	johnson	johnson	PROPN
fcis-25717	61	5	,	,	PUNCT
fcis-25717	61	6	david	david	PROPN
fcis-25717	61	7	,	,	PUNCT
fcis-25717	61	8	et	et	PROPN
fcis-25717	61	9	al	al	PROPN
fcis-25717	61	10	.	.	PUNCT
fcis-25717	62	1	"	"	PUNCT
fcis-25717	62	2	a	a	DET
fcis-25717	62	3	comparative	comparative	ADJ
fcis-25717	62	4	study	study	NOUN
fcis-25717	62	5	of	of	ADP
fcis-25717	62	6	lstm	lstm	PROPN
fcis-25717	62	7	and	and	CCONJ
fcis-25717	62	8	gru	gru	NOUN
fcis-25717	62	9	networks	network	NOUN
fcis-25717	62	10	.	.	PUNCT
fcis-25717	62	11	"	"	PUNCT
fcis-25717	62	12	carnegie	carnegie	PROPN
fcis-25717	62	13	mellon	mellon	PROPN
fcis-25717	62	14	university	university	PROPN
fcis-25717	62	15	,	,	PUNCT
fcis-25717	62	16	2023	2023	NUM
fcis-25717	62	17	,	,	PUNCT
fcis-25717	62	18	www	www	PROPN
fcis-25717	62	19	.	.	PROPN
fcis-25717	62	20	cmu.edu/	cmu.edu/	PROPN
fcis-25717	62	21	publications	publication	NOUN
fcis-25717	62	22	/	/	SYM
fcis-25717	62	23	lstm	lstm	PROPN
fcis-25717	62	24	-	-	PUNCT
fcis-25717	62	25	gru	gru	NOUN
fcis-25717	62	26	-	-	NOUN
fcis-25717	62	27	comparison	comparison	NOUN
fcis-25717	62	28	.	.	PUNCT
fcis-25717	63	1	[	[	X
fcis-25717	63	2	3	3	NUM
fcis-25717	63	3	]	]	X
fcis-25717	63	4	gates	gate	NOUN
fcis-25717	63	5	,	,	PUNCT
fcis-25717	63	6	mary	mary	PROPN
fcis-25717	63	7	.	.	PUNCT
fcis-25717	64	1	"	"	PUNCT
fcis-25717	64	2	bias	bias	NOUN
fcis-25717	64	3	in	in	ADP
fcis-25717	64	4	natural	natural	ADJ
fcis-25717	64	5	language	language	NOUN
fcis-25717	64	6	processing	processing	NOUN
fcis-25717	64	7	models	model	NOUN
fcis-25717	64	8	.	.	PUNCT
fcis-25717	64	9	"	"	PUNCT
fcis-25717	65	1	harvard	harvard	PROPN
fcis-25717	65	2	university	university	PROPN
fcis-25717	65	3	,	,	PUNCT
fcis-25717	65	4	2023	2023	NUM
fcis-25717	65	5	,	,	PUNCT
fcis-25717	65	6	www	www	PROPN
fcis-25717	65	7	.	.	PROPN
fcis-25717	65	8	harvard.edu/	harvard.edu/	PROPN
fcis-25717	65	9	research	research	NOUN
fcis-25717	65	10	/	/	SYM
fcis-25717	65	11	bias	bias	NOUN
fcis-25717	65	12	-	-	PUNCT
fcis-25717	65	13	nlp	nlp	NOUN
fcis-25717	65	14	-	-	PUNCT
fcis-25717	65	15	models	model	NOUN
fcis-25717	65	16	.	.	PUNCT
fcis-25717	66	1	[	[	X
fcis-25717	66	2	4	4	NUM
fcis-25717	66	3	]	]	X
fcis-25717	66	4	nguyen	nguyen	NOUN
fcis-25717	66	5	,	,	PUNCT
fcis-25717	66	6	linh	linh	INTJ
fcis-25717	66	7	.	.	PUNCT
fcis-25717	67	1	"	"	PUNCT
fcis-25717	67	2	mitigating	mitigate	VERB
fcis-25717	67	3	bias	bias	NOUN
fcis-25717	67	4	in	in	ADP
fcis-25717	67	5	machine	machine	NOUN
fcis-25717	67	6	learning	learning	NOUN
fcis-25717	67	7	:	:	PUNCT
fcis-25717	67	8	techniques	technique	NOUN
fcis-25717	67	9	and	and	CCONJ
fcis-25717	67	10	challenges	challenge	NOUN
fcis-25717	67	11	.	.	PUNCT
fcis-25717	67	12	"	"	PUNCT
fcis-25717	67	13	university	university	PROPN
fcis-25717	67	14	of	of	ADP
fcis-25717	67	15	california	california	PROPN
fcis-25717	67	16	,	,	PUNCT
fcis-25717	67	17	berkeley	berkeley	PROPN
fcis-25717	67	18	,	,	PUNCT
fcis-25717	67	19	2023	2023	NUM
fcis-25717	67	20	,	,	PUNCT
fcis-25717	67	21	www.berkeley.edu/research/bias-mitigationml	www.berkeley.edu/research/bias-mitigationml	NUM
fcis-25717	67	22	.	.	PUNCT