ACL 2022findings16 citations
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.
BibTeX
@inproceedings{guo-etal-2022-automatic,
title = "Automatic Song Translation for Tonal Languages",
author = "Guo, Fenfei and
Zhang, Chen and
Zhang, Zhirui and
He, Qixin and
Zhang, Kejun and
Xie, Jun and
Boyd-Graber, Jordan",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-acl.60/",
doi = "10.18653/v1/2022.findings-acl.60",
pages = "729--743"
}