Improving Japanese-to-English Neural Machine Translation by Voice Prediction
Hayahide Yamagishi, Shin Kanouchi, Takayuki Sato, Mamoru Komachi
Abstract
This study reports an attempt to predict the voice of reference using the information from the input sentences or previous input/output sentences. Our previous study presented a voice controlling method to generate sentences for neural machine translation, wherein it was demonstrated that the BLEU score improved when the voice of generated sentence was controlled relative to that of the reference. However, it is impractical to use the reference information because we cannot discern the voice of the correct translation in advance. Thus, this study presents a voice prediction method for generated sentences for neural machine translation. While evaluating on Japanese-to-English translation, we obtain a 0.70-improvement in the BLEU using the predicted voice.- Anthology ID:
- I17-2047
- Volume:
- Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
- Month:
- November
- Year:
- 2017
- Address:
- Taipei, Taiwan
- Venue:
- IJCNLP
- SIG:
- Publisher:
- Asian Federation of Natural Language Processing
- Note:
- Pages:
- 277–282
- Language:
- URL:
- https://aclanthology.org/I17-2047
- DOI:
- Cite (ACL):
- Hayahide Yamagishi, Shin Kanouchi, Takayuki Sato, and Mamoru Komachi. 2017. Improving Japanese-to-English Neural Machine Translation by Voice Prediction. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 277–282, Taipei, Taiwan. Asian Federation of Natural Language Processing.
- Cite (Informal):
- Improving Japanese-to-English Neural Machine Translation by Voice Prediction (Yamagishi et al., IJCNLP 2017)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/I17-2047.pdf
- Data
- ASPEC