Abstract
Unsupervised neural machine translation (NMT) utilizes only monolingual data for training. The quality of back-translated data plays an important role in the performance of NMT systems. In back-translation, all generated pseudo parallel sentence pairs are not of the same quality. Taking inspiration from domain adaptation where in-domain sentences are given more weight in training, in this paper we propose an approach to filter back-translated data as part of the training process of unsupervised NMT. Our approach gives more weight to good pseudo parallel sentence pairs in the back-translation phase. We calculate the weight of each pseudo parallel sentence pair using sentence-wise round-trip BLEU score which is normalized batch-wise. We compare our approach with the current state of the art approaches for unsupervised NMT.- Anthology ID:
- 2020.coling-main.383
- Volume:
- Proceedings of the 28th International Conference on Computational Linguistics
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Donia Scott, Nuria Bel, Chengqing Zong
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 4334–4339
- Language:
- URL:
- https://aclanthology.org/2020.coling-main.383
- DOI:
- 10.18653/v1/2020.coling-main.383
- Cite (ACL):
- Jyotsana Khatri and Pushpak Bhattacharyya. 2020. Filtering Back-Translated Data in Unsupervised Neural Machine Translation. In Proceedings of the 28th International Conference on Computational Linguistics, pages 4334–4339, Barcelona, Spain (Online). International Committee on Computational Linguistics.
- Cite (Informal):
- Filtering Back-Translated Data in Unsupervised Neural Machine Translation (Khatri & Bhattacharyya, COLING 2020)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2020.coling-main.383.pdf