# A Hierarchical Attention Based Seq2seq Model for Chinese Lyrics Generation

arXiv: https://arxiv.org/abs/1906.06481 · HTML: https://mlsanity.com/paper/1906.06481/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2019.

## Built on by (most cited first, 7 of 7)

- [Youling: an AI-Assisted Lyrics Creation System](https://mlsanity.com/paper/2201.06724/) (2022)
- [Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Ability](https://mlsanity.com/paper/2506.15629/) (2025)
- [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)
- [Syllable-level lyrics generation from melody exploiting character-level language model](https://mlsanity.com/paper/2310.00863/) (2023)
- [REFFLY: Melody-Constrained Lyrics Editing Model](https://mlsanity.com/paper/2409.00292/) (2024)
- [Sudowoodo: a Chinese Lyric Imitation System with Source Lyrics](https://mlsanity.com/paper/2308.04665/) (2023)

## Abstract (arXiv)

In this paper, we comprehensively study on context-aware generation of Chinese song lyrics. Conventional text generative models generate a sequence or sentence word by word, failing to consider the contextual relationship between sentences. Taking account into the characteristics of lyrics, a hierarchical attention based Seq2Seq (Sequence-to-Sequence) model is proposed for Chinese lyrics generation. With encoding of word-level and sentence-level contextual information, this model promotes the topic relevance and consistency of generation. A large Chinese lyrics corpus is also leveraged for model training. Eventually, results of automatic and human evaluations demonstrate that our model is able to compose complete Chinese lyrics with one united topic constraint.