Abstract
We present a simple new method where an emergent NMT system is used for simultaneously selecting training data and learning internal NMT representations. This is done in a self-supervised way without parallel data, in such a way that both tasks enhance each other during training. The method is language independent, introduces no additional hyper-parameters, and achieves BLEU scores of 29.21 (en2fr) and 27.36 (fr2en) on newstest2014 using English and French Wikipedia data for training.- Anthology ID:
- P19-1178
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1828–1834
- Language:
- URL:
- https://aclanthology.org/P19-1178
- DOI:
- 10.18653/v1/P19-1178
- Cite (ACL):
- Dana Ruiter, Cristina España-Bonet, and Josef van Genabith. 2019. Self-Supervised Neural Machine Translation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1828–1834, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Self-Supervised Neural Machine Translation (Ruiter et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/P19-1178.pdf
- Code
- ruitedk6/comparableNMT