@inproceedings{chang-etal-2023-sudowoodo,
title = "Sudowoodo: A {C}hinese Lyric Imitation System with Source Lyrics",
author = "Chang, Yongzhu and
Zhang, Rongsheng and
Jiang, Lin and
Chen, Qihang and
Zhang, Le and
Pu, Jiashu",
editor = "Feng, Yansong and
Lefever, Els",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-demo.8",
doi = "10.18653/v1/2023.emnlp-demo.8",
pages = "99--105",
abstract = "Lyrics generation is a well-known application in natural language generation research, with several previous studies focusing on generating accurate lyrics using precise control such as keywords, rhymes, etc. However, lyrics imitation, which involves writing new lyrics by imitating the style and content of the source lyrics, remains a challenging task due to the lack of a parallel corpus. In this paper, we introduce Sudowoodo, a Chinese lyrics imitation system that can generate new lyrics based on the text of source lyrics. To address the issue of lacking a parallel training corpus for lyrics imitation, we propose a novel framework to construct a parallel corpus based on a keyword-based lyrics model from source lyrics. Then the pairs \textit{(new lyrics, source lyrics)} are used to train the lyrics imitation model. During the inference process, we utilize a post-processing module to filter and rank the generated lyrics, selecting the highest-quality ones. We incorporated audio information and aligned the lyrics with the audio to form the songs as a bonus. The human evaluation results show that our framework can perform better lyric imitation. Meanwhile, the \textit{Sudowoodo} system and demo video of the system is available at Sudowoodo and \url{https://youtu.be/u5BBT\_j1L5M}",
}
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<abstract>Lyrics generation is a well-known application in natural language generation research, with several previous studies focusing on generating accurate lyrics using precise control such as keywords, rhymes, etc. However, lyrics imitation, which involves writing new lyrics by imitating the style and content of the source lyrics, remains a challenging task due to the lack of a parallel corpus. In this paper, we introduce Sudowoodo, a Chinese lyrics imitation system that can generate new lyrics based on the text of source lyrics. To address the issue of lacking a parallel training corpus for lyrics imitation, we propose a novel framework to construct a parallel corpus based on a keyword-based lyrics model from source lyrics. Then the pairs (new lyrics, source lyrics) are used to train the lyrics imitation model. During the inference process, we utilize a post-processing module to filter and rank the generated lyrics, selecting the highest-quality ones. We incorporated audio information and aligned the lyrics with the audio to form the songs as a bonus. The human evaluation results show that our framework can perform better lyric imitation. Meanwhile, the Sudowoodo system and demo video of the system is available at Sudowoodo and https://youtu.be/u5BBT_j1L5M</abstract>
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%0 Conference Proceedings
%T Sudowoodo: A Chinese Lyric Imitation System with Source Lyrics
%A Chang, Yongzhu
%A Zhang, Rongsheng
%A Jiang, Lin
%A Chen, Qihang
%A Zhang, Le
%A Pu, Jiashu
%Y Feng, Yansong
%Y Lefever, Els
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F chang-etal-2023-sudowoodo
%X Lyrics generation is a well-known application in natural language generation research, with several previous studies focusing on generating accurate lyrics using precise control such as keywords, rhymes, etc. However, lyrics imitation, which involves writing new lyrics by imitating the style and content of the source lyrics, remains a challenging task due to the lack of a parallel corpus. In this paper, we introduce Sudowoodo, a Chinese lyrics imitation system that can generate new lyrics based on the text of source lyrics. To address the issue of lacking a parallel training corpus for lyrics imitation, we propose a novel framework to construct a parallel corpus based on a keyword-based lyrics model from source lyrics. Then the pairs (new lyrics, source lyrics) are used to train the lyrics imitation model. During the inference process, we utilize a post-processing module to filter and rank the generated lyrics, selecting the highest-quality ones. We incorporated audio information and aligned the lyrics with the audio to form the songs as a bonus. The human evaluation results show that our framework can perform better lyric imitation. Meanwhile, the Sudowoodo system and demo video of the system is available at Sudowoodo and https://youtu.be/u5BBT_j1L5M
%R 10.18653/v1/2023.emnlp-demo.8
%U https://aclanthology.org/2023.emnlp-demo.8
%U https://doi.org/10.18653/v1/2023.emnlp-demo.8
%P 99-105
Markdown (Informal)
[Sudowoodo: A Chinese Lyric Imitation System with Source Lyrics](https://aclanthology.org/2023.emnlp-demo.8) (Chang et al., EMNLP 2023)
ACL
- Yongzhu Chang, Rongsheng Zhang, Lin Jiang, Qihang Chen, Le Zhang, and Jiashu Pu. 2023. Sudowoodo: A Chinese Lyric Imitation System with Source Lyrics. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 99–105, Singapore. Association for Computational Linguistics.