DeepLyricist: Automatic Generation of Rap Lyrics using Sequence-to-Sequence Learning Nils Hulzebosch Student ID: 10749411 A thesis presented for the degree of Bachelor Artificial Intelligence 18 ECTS Faculty of Science University of Amsterdam Netherlands July 2, 2017 M.Sc. Mostafa Dehghani Dr. Sander van Splunter University of Amsterdam University of Amsterdam
[email protected] [email protected] First supervisor Second supervisor Abstract This thesis demonstrates the use of sequence-to-sequence learning for the automatic generation of novel English rap lyrics, with the goal of generating lyrics with similar qualities to those of humans in terms of rhyme, idiom, structure, and novelty. The sequence-to-sequence model tries to learn the best parameters for generating target sequences given source sequences, and is trained on over 1.6 million source-target pairs, containing lyrics from 348 different rap artists. The automatic evaluations of each of the four characteristics show that idiom and structure have the best performance, whereas rhyme and novelty should be improved to be in a similar range of human lyrics. Future research could focus on using hierarchical models to improve the learning and generation of rhyme, structure, and possibly novelty, and implementing a sampled probability to increase the uniqueness of generated lyrics. 1 Contents Acknowledgements 4 1 Introduction 5 2 Related work 6 3 Characteristics of rap lyrics 7 3.1 Rhyme . .7 3.2 Rhyme schemes . .9 3.3 Song structure . 10 3.4 Idiom . 11 3.5 Novelty . 12 4 Evaluation Methodology 12 4.1 Rhyme . 12 4.1.1 Modified rhyme density . 13 4.1.2 End rhyme score .