Abstract
We participated in the WMT 2018 shared news translation task on English↔Chinese language pair. Our systems are based on attentional sequence-to-sequence models with some form of recursion and self-attention. Some data augmentation methods are also introduced to improve the translation performance. The best translation result is obtained with ensemble and reranking techniques. Our Chinese→English system achieved the highest cased BLEU score among all 16 submitted systems, and our English→Chinese system ranked the third out of 18 submitted systems.- Anthology ID:
- W18-6429
- 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:
- 522–527
- Language:
- URL:
- https://aclanthology.org/W18-6429
- DOI:
- 10.18653/v1/W18-6429
- Cite (ACL):
- Mingxuan Wang, Li Gong, Wenhuan Zhu, Jun Xie, and Chao Bian. 2018. Tencent Neural Machine Translation Systems for WMT18. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 522–527, Belgium, Brussels. Association for Computational Linguistics.
- Cite (Informal):
- Tencent Neural Machine Translation Systems for WMT18 (Wang et al., WMT 2018)
- PDF:
- https://preview.aclanthology.org/author-url/W18-6429.pdf