# Evaluating Creative Language Generation: The Case of Rap Lyric Ghostwriting

arXiv: https://arxiv.org/abs/1612.03205 · HTML: https://mlsanity.com/paper/1612.03205/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2016.

## Built on by (most cited first, 3 of 3)

- [Youling: an AI-Assisted Lyrics Creation System](https://mlsanity.com/paper/2201.06724/) (2022)
- [Train & Constrain: Phonologically Informed Tongue-Twister Generation from Topics and Paraphrases](https://mlsanity.com/paper/2403.13901/) (2024)
- [A General Framework for Learning Prosodic-Enhanced Representation of Rap Lyrics](https://mlsanity.com/paper/2103.12615/) (2021)

## Abstract (arXiv)

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 evaluation methods for one such task, ghostwriting of rap lyrics, and to provide an explicit, quantifiable foundation for the goals and future directions of 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 meaningfully analyze system performance. We provide a corpus of lyrics for 13 rap artists, annotated for stylistic similarity, which allows us to assess the feasibility of manual evaluation for generated verse.