Lexical Translation Inconsistency-Aware Document-Level Translation Repair
Zhen Zhang, Junhui Li, Shimin Tao, Hao Yang
Abstract
Following the idea of “one translation per discourse”, in this paper we aim to improve translation consistency via document-level translation repair (DocRepair), i.e., automatic post-editing on translations of documents. To this end, we propose a lexical translation inconsistency-aware DocRepair to explicitly model translation inconsistency. First we locate the inconsistency in automatic translation. Then we provide translation candidates for those inconsistency. Finally, we propose lattice-like input to properly model inconsistent tokens and phrases and their candidates. Experimental results on three document-level translation datasets show that based on G-Transformer, a state-of-the-art document-to-document (Doc2Doc) translation model, our Doc2Doc DocRepair achieves significant improvement on translation quality in BLEU scores, but also greatly improves lexical translation consistency.
BibTeX
@inproceedings{zhang-etal-2023-lexical,
title = "Lexical Translation Inconsistency-Aware Document-Level Translation Repair",
author = "Zhang, Zhen and
Li, Junhui and
Tao, Shimin and
Yang, Hao",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.791/",
doi = "10.18653/v1/2023.findings-acl.791",
pages = "12492--12505"
}