Evaluating Creative Language Generation: The Case of Rap Lyric Ghostwriting

Language generation tasks that seek to mimic human ability to use language creatively are difficult to evaluate, since one must consider creativity, style, and other non-trivial aspects of the generated text. The goal of this paper is to develop evaluations methods for one such task, ghostwriting of rap lyrics, and to provide an explicit, quantifiable foundation for the goals and future directions for this task.Ghostwriting must produce text that is similar in style to the emulated artist, yet distinct in content. We develop a novel evaluation methodology that addresses several complementary aspects of this task, and illustrate how such evaluation can be used to meaning-fully analyze system performance. We provide a corpus of lyrics for 13 rap artists, annotated for stylistic similarity, which allows us to as-sess the feasibility of manual evaluation for generated verse.


P. Potash A. Romanov A. Rumshisky GhostWriter: Using an LSTM for Automatic Rap Lyric Generation. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics. 2015

@inproceedings{potash_ghostwriter:_2015, author = "Potash, Peter and Romanov, Alexey and Rumshisky, Anna", address = "Lisbon, Portugal", title = "{GhostWriter}: {Using} an {LSTM} for {Automatic} {Rap} {Lyric} {Generation}", doi = "10.18653/v1/D15-1221", booktitle = "Proceedings of the 2015 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing}", publisher = "Association for Computational Linguistics", year = "2015", pages = "1919--1924" }