Automatic Song Translation for Tonal Languages

被引：0

作者：

Guo, Fenfei ^{[1
]}

Zhang, Chen ^{[2
]}

Zhang, Zhirui ^{[3
]}

He, Qixin ^{[4
]}

Zhang, Kejun ^{[2
]}

Xie, Jun ^{[5
]}

Boyd-Graber, Jordan ^{[6
]}

机构：

[1] Univ Maryland, College Pk, MD 20742 USA

[2] Zhejiang Univ, Hangzhou, Peoples R China

[3] Tencent AI Lab, Bellevue, WA USA

[4] Purdue Univ, W Lafayette, IN 47907 USA

[5] Alibaba DAMO Acad, Hangzhou, Peoples R China

[6] Univ Maryland, CS, iSch, UMIACS,LSC, College Pk, MD 20742 USA

来源：

FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2022) | 2022年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：729 / 743

页数：15

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