Supervised neural machine translation based on data augmentation and improved training & inference process
Yixuan Tong, Liang Liang, Boyan Liu, Shanshan Jiang, Bin Dong
Abstract
This is the second time for SRCB to participate in WAT. This paper describes the neural machine translation systems for the shared translation tasks of WAT 2019. We participated in ASPEC tasks and submitted results on English-Japanese, Japanese-English, Chinese-Japanese, and Japanese-Chinese four language pairs. We employed the Transformer model as the baseline and experimented relative position representation, data augmentation, deep layer model, ensemble. Experiments show that all these methods can yield substantial improvements.- Anthology ID:
- D19-5218
- Volume:
- Proceedings of the 6th Workshop on Asian Translation
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Venue:
- WAT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 147–151
- Language:
- URL:
- https://aclanthology.org/D19-5218
- DOI:
- 10.18653/v1/D19-5218
- Cite (ACL):
- Yixuan Tong, Liang Liang, Boyan Liu, Shanshan Jiang, and Bin Dong. 2019. Supervised neural machine translation based on data augmentation and improved training & inference process. In Proceedings of the 6th Workshop on Asian Translation, pages 147–151, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Supervised neural machine translation based on data augmentation and improved training & inference process (Tong et al., WAT 2019)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/D19-5218.pdf