Controlling the Voice of a Sentence in Japanese-to-English Neural Machine Translation

Hayahide Yamagishi, Shin Kanouchi, Takayuki Sato, Mamoru Komachi


Abstract
In machine translation, we must consider the difference in expression between languages. For example, the active/passive voice may change in Japanese-English translation. The same verb in Japanese may be translated into different voices at each translation because the voice of a generated sentence cannot be determined using only the information of the Japanese sentence. Machine translation systems should consider the information structure to improve the coherence of the output by using several topicalization techniques such as passivization. Therefore, this paper reports on our attempt to control the voice of the sentence generated by an encoder-decoder model. To control the voice of the generated sentence, we added the voice information of the target sentence to the source sentence during the training. We then generated sentences with a specified voice by appending the voice information to the source sentence. We observed experimentally whether the voice could be controlled. The results showed that, we could control the voice of the generated sentence with 85.0% accuracy on average. In the evaluation of Japanese-English translation, we obtained a 0.73-point improvement in BLEU score by using gold voice labels.
Anthology ID:
W16-4620
Volume:
Proceedings of the 3rd Workshop on Asian Translation (WAT2016)
Month:
December
Year:
2016
Address:
Osaka, Japan
Venues:
WAT | WS
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
203–210
Language:
URL:
https://aclanthology.org/W16-4620
DOI:
Bibkey:
Cite (ACL):
Hayahide Yamagishi, Shin Kanouchi, Takayuki Sato, and Mamoru Komachi. 2016. Controlling the Voice of a Sentence in Japanese-to-English Neural Machine Translation. In Proceedings of the 3rd Workshop on Asian Translation (WAT2016), pages 203–210, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
Controlling the Voice of a Sentence in Japanese-to-English Neural Machine Translation (Yamagishi et al., 2016)
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PDF:
https://aclanthology.org/W16-4620.pdf
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