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://www.aclweb.org/anthology/I17-2047
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/I17-2047.pdf