BitextEdit: Automatic Bitext Editing for Improved Low-Resource Machine Translation

Eleftheria Briakou, Sida Wang, Luke Zettlemoyer, Marjan Ghazvininejad


Abstract
Mined bitexts can contain imperfect translations that yield unreliable training signals for Neural Machine Translation (NMT). While filtering such pairs out is known to improve final model quality, we argue that it is suboptimal in low-resource conditions where even mined data can be limited. In our work, we propose instead, to refine the mined bitexts via automatic editing: given a sentence in a language xf, and a possibly imperfect translation of it xe, our model generates a revised version xf' or xe' that yields a more equivalent translation pair (i.e., <xf, xe'> or <xf', xe>). We use a simple editing strategy by (1) mining potentially imperfect translations for each sentence in a given bitext, (2) learning a model to reconstruct the original translations and translate, in a multi-task fashion. Experiments demonstrate that our approach successfully improves the quality of CCMatrix mined bitext for 5 low-resource language-pairs and 10 translation directions by up to 8 BLEU points, in most cases improving upon a competitive translation-based baseline.
Anthology ID:
2022.findings-naacl.110
Volume:
Findings of the Association for Computational Linguistics: NAACL 2022
Month:
July
Year:
2022
Address:
Seattle, United States
Editors:
Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1469–1485
Language:
URL:
https://aclanthology.org/2022.findings-naacl.110
DOI:
10.18653/v1/2022.findings-naacl.110
Bibkey:
Cite (ACL):
Eleftheria Briakou, Sida Wang, Luke Zettlemoyer, and Marjan Ghazvininejad. 2022. BitextEdit: Automatic Bitext Editing for Improved Low-Resource Machine Translation. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 1469–1485, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
BitextEdit: Automatic Bitext Editing for Improved Low-Resource Machine Translation (Briakou et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-naacl.110.pdf
Video:
 https://aclanthology.org/2022.findings-naacl.110.mp4
Data
CCMatrixFLoResOpenSubtitlesParaCrawlWikiMatrix