Qixin He
2022
Automatic Song Translation for Tonal Languages
Fenfei Guo
|
Chen Zhang
|
Zhirui Zhang
|
Qixin He
|
Kejun Zhang
|
Jun Xie
|
Jordan Boyd-Graber
Findings of the Association for Computational Linguistics: ACL 2022
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.
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Co-authors
- Fenfei Guo 1
- Chen Zhang 1
- Zhirui Zhang 1
- Kejun Zhang 1
- Jun Xie 1
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