Abstract
In this paper, we show that neural machine translation (NMT) systems trained on large back-translated data overfit some of the characteristics of machine-translated texts. Such NMT systems better translate human-produced translations, i.e., translationese, but may largely worsen the translation quality of original texts. Our analysis reveals that adding a simple tag to back-translations prevents this quality degradation and improves on average the overall translation quality by helping the NMT system to distinguish back-translated data from original parallel data during training. We also show that, in contrast to high-resource configurations, NMT systems trained in low-resource settings are much less vulnerable to overfit back-translations. We conclude that the back-translations in the training data should always be tagged especially when the origin of the text to be translated is unknown.- Anthology ID:
- 2020.acl-main.532
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5990–5997
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.532
- DOI:
- 10.18653/v1/2020.acl-main.532
- Cite (ACL):
- Benjamin Marie, Raphael Rubino, and Atsushi Fujita. 2020. Tagged Back-translation Revisited: Why Does It Really Work?. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5990–5997, Online. Association for Computational Linguistics.
- Cite (Informal):
- Tagged Back-translation Revisited: Why Does It Really Work? (Marie et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.acl-main.532.pdf