@inproceedings{zhao-etal-2025-reffly,
title = "{REFFLY}: Melody-Constrained Lyrics Editing Model",
author = "Zhao, Songyan and
Li, Bingxuan and
Tian, Yufei and
Peng, Nanyun",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.564/",
doi = "10.18653/v1/2025.naacl-long.564",
pages = "11295--11315",
ISBN = "979-8-89176-189-6",
abstract = "Automatic melody-to-lyric (M2L) generation aims to create lyrics that align with a given melody. While most previous approaches generate lyrics from scratch, revision{---}editing plain text draft to fit it into the melody{---}offers a much more flexible and practical alternative. This enables broad applications, such as generating lyrics from flexible inputs (keywords, themes, or full text that needs refining to be singable), song translation (preserving meaning across languages while keeping the melody intact), or style transfer (adapting lyrics to different genres). This paper introduces REFFLY (REvision Framework For LYrics), the first revision framework for editing and generating melody-aligned lyrics. We train the lyric revision module using our curated synthesized melody-aligned lyrics dataset, enabling it to transform plain text into lyrics that align with a given melody. To further enhance the revision ability, we propose training-free heuristics aimed at preserving both semantic meaning and musical consistency throughout the editing process. Experimental results demonstrate the effectiveness of REFFLY across various tasks (e.g. song translation), showing that our model outperforms strong baselines, including Lyra (CITATION) and GPT-4, by 25{\%} in both musicality and text quality."
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<abstract>Automatic melody-to-lyric (M2L) generation aims to create lyrics that align with a given melody. While most previous approaches generate lyrics from scratch, revision—editing plain text draft to fit it into the melody—offers a much more flexible and practical alternative. This enables broad applications, such as generating lyrics from flexible inputs (keywords, themes, or full text that needs refining to be singable), song translation (preserving meaning across languages while keeping the melody intact), or style transfer (adapting lyrics to different genres). This paper introduces REFFLY (REvision Framework For LYrics), the first revision framework for editing and generating melody-aligned lyrics. We train the lyric revision module using our curated synthesized melody-aligned lyrics dataset, enabling it to transform plain text into lyrics that align with a given melody. To further enhance the revision ability, we propose training-free heuristics aimed at preserving both semantic meaning and musical consistency throughout the editing process. Experimental results demonstrate the effectiveness of REFFLY across various tasks (e.g. song translation), showing that our model outperforms strong baselines, including Lyra (CITATION) and GPT-4, by 25% in both musicality and text quality.</abstract>
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%0 Conference Proceedings
%T REFFLY: Melody-Constrained Lyrics Editing Model
%A Zhao, Songyan
%A Li, Bingxuan
%A Tian, Yufei
%A Peng, Nanyun
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F zhao-etal-2025-reffly
%X Automatic melody-to-lyric (M2L) generation aims to create lyrics that align with a given melody. While most previous approaches generate lyrics from scratch, revision—editing plain text draft to fit it into the melody—offers a much more flexible and practical alternative. This enables broad applications, such as generating lyrics from flexible inputs (keywords, themes, or full text that needs refining to be singable), song translation (preserving meaning across languages while keeping the melody intact), or style transfer (adapting lyrics to different genres). This paper introduces REFFLY (REvision Framework For LYrics), the first revision framework for editing and generating melody-aligned lyrics. We train the lyric revision module using our curated synthesized melody-aligned lyrics dataset, enabling it to transform plain text into lyrics that align with a given melody. To further enhance the revision ability, we propose training-free heuristics aimed at preserving both semantic meaning and musical consistency throughout the editing process. Experimental results demonstrate the effectiveness of REFFLY across various tasks (e.g. song translation), showing that our model outperforms strong baselines, including Lyra (CITATION) and GPT-4, by 25% in both musicality and text quality.
%R 10.18653/v1/2025.naacl-long.564
%U https://aclanthology.org/2025.naacl-long.564/
%U https://doi.org/10.18653/v1/2025.naacl-long.564
%P 11295-11315
Markdown (Informal)
[REFFLY: Melody-Constrained Lyrics Editing Model](https://aclanthology.org/2025.naacl-long.564/) (Zhao et al., NAACL 2025)
ACL
- Songyan Zhao, Bingxuan Li, Yufei Tian, and Nanyun Peng. 2025. REFFLY: Melody-Constrained Lyrics Editing Model. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 11295–11315, Albuquerque, New Mexico. Association for Computational Linguistics.