Translate the Beauty in Songs: Jointly Learning to Align Melody and Translate Lyrics

Chengxi Li, Kai Fan, Jiajun Bu, Boxing Chen, Zhongqiang Huang, Zhi Yu

Abstract
Song translation requires both translation of lyrics and alignment of music notes so that the resulting verse can be sung to the accompanying melody, which is a challenging problem that has attracted some interests in different aspects of the translation process. In this paper, we propose Lyrics-Melody Translation with Adaptive Grouping (LTAG), a holistic solution to automatic song translation by jointly modeling lyric translation and lyrics-melody alignment. It is a novel encoder-decoder framework that can simultaneously translate the source lyrics and determine the number of aligned notes at each decoding step through an adaptive note grouping module. To address data scarcity, we commissioned a small amount of training data annotated specifically for this task and used large amounts of automatic training data through back-translation. Experiments conducted on an English-Chinese song translation data set show the effectiveness of our model in both automatic and human evaluations.
Anthology ID:
2023.findings-emnlp.3
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
27–39
Language:
URL:
https://aclanthology.org/2023.findings-emnlp.3
DOI:
10.18653/v1/2023.findings-emnlp.3
Bibkey:
Cite (ACL):
Chengxi Li, Kai Fan, Jiajun Bu, Boxing Chen, Zhongqiang Huang, and Zhi Yu. 2023. Translate the Beauty in Songs: Jointly Learning to Align Melody and Translate Lyrics. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 27–39, Singapore. Association for Computational Linguistics.
Cite (Informal):
Translate the Beauty in Songs: Jointly Learning to Align Melody and Translate Lyrics (Li et al., Findings 2023)
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