Easy Guided Decoding in Providing Suggestions for Interactive Machine Translation

Ke Wang, Xin Ge, Jiayi Wang, Yuqi Zhang, Yu Zhao


Abstract
Machine translation technology has made great progress in recent years, but it cannot guarantee error-free results. Human translators perform post-editing on machine translations to correct errors in the scene of computer aided translation. In favor of expediting the post-editing process, many works have investigated machine translation in interactive modes, in which machines can automatically refine the rest of translations constrained by human’s edits. Translation Suggestion (TS), as an interactive mode to assist human translators, requires machines to generate alternatives for specific incorrect words or phrases selected by human translators. In this paper, we utilize the parameterized objective function of neural machine translation (NMT) and propose a novel constrained decoding algorithm, namely Prefix-Suffix Guided Decoding (PSGD), to deal with the TS problem without additional training. Compared to state-of-the-art lexical-constrained decoding method, PSGD improves translation quality by an average of 10.6 BLEU and reduces time overhead by an average of 63.4% on benchmark datasets. Furthermore, on both the WeTS and the WMT 2022 Translation Suggestion datasets, it is superior over other supervised learning systems trained with TS annotated data.
Anthology ID:
2023.acl-long.434
Volume:
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7840–7852
Language:
URL:
https://aclanthology.org/2023.acl-long.434
DOI:
10.18653/v1/2023.acl-long.434
Bibkey:
Cite (ACL):
Ke Wang, Xin Ge, Jiayi Wang, Yuqi Zhang, and Yu Zhao. 2023. Easy Guided Decoding in Providing Suggestions for Interactive Machine Translation. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7840–7852, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
Easy Guided Decoding in Providing Suggestions for Interactive Machine Translation (Wang et al., ACL 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.acl-long.434.pdf
Video:
 https://aclanthology.org/2023.acl-long.434.mp4