# DopeLearning: A Computational Approach to Rap Lyrics Generation

arXiv: https://arxiv.org/abs/1505.04771 · HTML: https://mlsanity.com/paper/1505.04771/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2015.

## Built on by (most cited first, 17 of 17)

- [DeepRapper: Neural Rap Generation with Rhyme and Rhythm Modeling](https://mlsanity.com/paper/2107.01875/) (2021)
- [TeleMelody: Lyric-to-Melody Generation with a Template-Based Two-Stage Method](https://mlsanity.com/paper/2109.09617/) (2021)
- [Automatic Song Translation for Tonal Languages](https://mlsanity.com/paper/2203.13420/) (2022)
- [SongComposer: A Large Language Model for Lyric and Melody Generation in Song Composition](https://mlsanity.com/paper/2402.17645/) (2024)
- [Say What? Collaborative Pop Lyric Generation Using Multitask Transfer Learning](https://mlsanity.com/paper/2111.07592/) (2021)
- [Re-creation of Creations: A New Paradigm for Lyric-to-Melody Generation](https://mlsanity.com/paper/2208.05697/) (2022)
- [Augmenting Poetry Composition with Verse by Verse](https://mlsanity.com/paper/2103.17205/) (2021)
- [LyricJam: A system for generating lyrics for live instrumental music](https://mlsanity.com/paper/2106.01960/) (2021)
- [Multi-Modal Experience Inspired AI Creation](https://mlsanity.com/paper/2209.02427/) (2022)
- [SongRewriter: A Chinese Song Rewriting System with Controllable Content and Rhyme Scheme](https://mlsanity.com/paper/2211.15037/) (2022)
- [LyricJam Sonic: A Generative System for Real-Time Composition and Musical Improvisation](https://mlsanity.com/paper/2210.15638/) (2022)
- [Automatic Generation of Product Concepts from Positive Examples, with an Application to Music Streaming](https://mlsanity.com/paper/2210.01515/) (2022)
- [CoLyricist: Enhancing Lyric Writing with AI through Workflow-Aligned Support](https://mlsanity.com/paper/2602.22606/) (2026)
- [A General Framework for Learning Prosodic-Enhanced Representation of Rap Lyrics](https://mlsanity.com/paper/2103.12615/) (2021)
- [Bridging Music and Text with Crowdsourced Music Comments: A Sequence-to-Sequence Framework for Thematic Music Comments Generation](https://mlsanity.com/paper/2209.01996/) (2022)
- [MCMChaos: Improvising Rap Music with MCMC Methods and Chaos Theory](https://mlsanity.com/paper/2401.07967/) (2024)
- [Raply: A profanity-mitigated rap generator](https://mlsanity.com/paper/2407.06941/) (2024)

## Abstract (arXiv)

Writing rap lyrics requires both creativity to construct a meaningful, interesting story and lyrical skills to produce complex rhyme patterns, which form the cornerstone of good flow. We present a rap lyrics generation method that captures both of these aspects. First, we develop a prediction model to identify the next line of existing lyrics from a set of candidate next lines. This model is based on two machine-learning techniques: the RankSVM algorithm and a deep neural network model with a novel structure. Results show that the prediction model can identify the true next line among 299 randomly selected lines with an accuracy of 17%, i.e., over 50 times more likely than by random. Second, we employ the prediction model to combine lines from existing songs, producing lyrics with rhyme and a meaning. An evaluation of the produced lyrics shows that in terms of quantitative rhyme density, the method outperforms the best human rappers by 21%. The rap lyrics generator has been deployed as an online tool called DeepBeat, and the performance of the tool has been assessed by analyzing its usage logs. This analysis shows that machine-learned rankings correlate with user preferences.