Automatic Song Translation for Tonal Languages

Fenfei Guo, Chen Zhang, Zhirui Zhang, Qixin He, Kejun Zhang, Jun Xie, Jordan Boyd-Graber


Abstract
This paper develops automatic song translation (AST) for tonal languages and addresses the unique challenge of aligning words’ tones with melody of a song in addition to conveying the original meaning. We propose three criteria for effective AST—preserving meaning, singability and intelligibility—and design metrics for these criteria. We develop a new benchmark for English–Mandarin song translation and develop an unsupervised AST system, Guided AliGnment for Automatic Song Translation (GagaST), which combines pre-training with three decoding constraints. Both automatic and human evaluations show GagaST successfully balances semantics and singability.
Anthology ID:
2022.findings-acl.60
Volume:
Findings of the Association for Computational Linguistics: ACL 2022
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Smaranda Muresan, Preslav Nakov, Aline Villavicencio
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
729–743
Language:
URL:
https://aclanthology.org/2022.findings-acl.60
DOI:
10.18653/v1/2022.findings-acl.60
Bibkey:
Cite (ACL):
Fenfei Guo, Chen Zhang, Zhirui Zhang, Qixin He, Kejun Zhang, Jun Xie, and Jordan Boyd-Graber. 2022. Automatic Song Translation for Tonal Languages. In Findings of the Association for Computational Linguistics: ACL 2022, pages 729–743, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Automatic Song Translation for Tonal Languages (Guo et al., Findings 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/2022.findings-acl.60.pdf
Video:
 https://preview.aclanthology.org/ingest-acl-2023-videos/2022.findings-acl.60.mp4