Kejun Zhang


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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.