Abstract
Neural machine translation (NMT) produces sentences that are more fluent than those produced by statistical machine translation (SMT). However, NMT has a very high computational cost because of the high dimensionality of the output layer. Generally, NMT restricts the size of vocabulary, which results in infrequent words being treated as out-of-vocabulary (OOV) and degrades the performance of the translation. In evaluation, we achieved a statistically significant BLEU score improvement of 0.55-0.77 over the baselines including the state-of-the-art method.- Anthology ID:
- W17-5703
- Volume:
- Proceedings of the 4th Workshop on Asian Translation (WAT2017)
- Month:
- November
- Year:
- 2017
- Address:
- Taipei, Taiwan
- Editors:
- Toshiaki Nakazawa, Isao Goto
- Venue:
- WAT
- SIG:
- Publisher:
- Asian Federation of Natural Language Processing
- Note:
- Pages:
- 64–69
- Language:
- URL:
- https://aclanthology.org/W17-5703
- DOI:
- Cite (ACL):
- Yuuki Sekizawa, Tomoyuki Kajiwara, and Mamoru Komachi. 2017. Improving Japanese-to-English Neural Machine Translation by Paraphrasing the Target Language. In Proceedings of the 4th Workshop on Asian Translation (WAT2017), pages 64–69, Taipei, Taiwan. Asian Federation of Natural Language Processing.
- Cite (Informal):
- Improving Japanese-to-English Neural Machine Translation by Paraphrasing the Target Language (Sekizawa et al., WAT 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/W17-5703.pdf
- Data
- ASPEC