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://preview.aclanthology.org/jlcl-multiple-ingestion/2022.findings-naacl.110/
- DOI:
- 10.18653/v1/2022.findings-naacl.110
- 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)
- PDF:
- https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.findings-naacl.110.pdf
- Data
- CCMatrix, FLoRes, OpenSubtitles, ParaCrawl, WikiMatrix