id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
fcis-25717	Liu, Lijie	Exploring Deep Learning Models for Lyric Generation and Addressing Biases in Word Embeddings	2024	3	.pdf	application/pdf	1265	74	44	Exploring Deep Learning Models for Lyric Generation and Addressing Biases in Word Embeddings Lijie Liu Rensselaer Polytechnic Institute Troy, Troy, New York, 12180 USA Abstract: The aim of this project is to explore the performance of different model architectures (such as RNN, LSTM, GRU) by generating lyrics using deep learning models, and to use the Word2Vec model for distributed semantic analysis to understand semantic phenomena and potential biases in word embedding models. 3. Results and Discussion Through training and comparing Vanilla RNN, LSTM, and GRU models, we found that LSTM and GRU exhibit better performance in processing long sequence data, specifically in terms of faster convergence speed and higher quality generated lyrics text.	cache/fcis-25717.pdf	txt/fcis-25717.txt