Abstract
Synthetic translations have been used for a wide range of NLP tasks primarily as a means of data augmentation. This work explores, instead, how synthetic translations can be used to revise potentially imperfect reference translations in mined bitext. We find that synthetic samples can improve bitext quality without any additional bilingual supervision when they replace the originals based on a semantic equivalence classifier that helps mitigate NMT noise. The improved quality of the revised bitext is confirmed intrinsically via human evaluation and extrinsically through bilingual induction and MT tasks.- Anthology ID:
- 2022.acl-long.326
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4753–4766
- Language:
- URL:
- https://aclanthology.org/2022.acl-long.326
- DOI:
- 10.18653/v1/2022.acl-long.326
- Cite (ACL):
- Eleftheria Briakou and Marine Carpuat. 2022. Can Synthetic Translations Improve Bitext Quality?. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4753–4766, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Can Synthetic Translations Improve Bitext Quality? (Briakou & Carpuat, ACL 2022)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.acl-long.326.pdf
- Data
- WikiMatrix