Abstract
This paper describes NTT’s neural machine translation systems submitted to the WMT 2018 English-German and German-English news translation tasks. Our submission has three main components: the Transformer model, corpus cleaning, and right-to-left n-best re-ranking techniques. Through our experiments, we identified two keys for improving accuracy: filtering noisy training sentences and right-to-left re-ranking. We also found that the Transformer model requires more training data than the RNN-based model, and the RNN-based model sometimes achieves better accuracy than the Transformer model when the corpus is small.- Anthology ID:
- W18-6421
- Volume:
- Proceedings of the Third Conference on Machine Translation: Shared Task Papers
- Month:
- October
- Year:
- 2018
- Address:
- Belgium, Brussels
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 461–466
- Language:
- URL:
- https://aclanthology.org/W18-6421
- DOI:
- 10.18653/v1/W18-6421
- Cite (ACL):
- Makoto Morishita, Jun Suzuki, and Masaaki Nagata. 2018. NTT’s Neural Machine Translation Systems for WMT 2018. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 461–466, Belgium, Brussels. Association for Computational Linguistics.
- Cite (Informal):
- NTT’s Neural Machine Translation Systems for WMT 2018 (Morishita et al., WMT 2018)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/W18-6421.pdf
- Data
- WMT 2018