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    <abstract>Character-level translation has been proved to be able to achieve preferable translation quality without explicit segmentation, but training a character-level model needs a lot of hardware resources. In this paper, we introduced two character-level translation models which are mid-gated model and multi-attention model for Japanese-English translation. We showed that the mid-gated model achieved the better performance with respect to BLEU scores. We also showed that a relatively narrow beam of width 4 or 5 was sufficient for the mid-gated model. As for unknown words, we showed that the mid-gated model could somehow translate the one containing Katakana by coining out a close word. We also showed that the model managed to produce tolerable results for heavily noised sentences, even though the model was trained with the dataset without noise.</abstract>
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%0 Conference Proceedings
%T Compact and Robust Models for Japanese-English Character-level Machine Translation
%A Dai, Jinan
%A Yamaguchi, Kazunori
%Y Nakazawa, Toshiaki
%Y Ding, Chenchen
%Y Dabre, Raj
%Y Kunchukuttan, Anoop
%Y Doi, Nobushige
%Y Oda, Yusuke
%Y Bojar, Ondřej
%Y Parida, Shantipriya
%Y Goto, Isao
%Y Mino, Hidaya
%S Proceedings of the 6th Workshop on Asian Translation
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F dai-yamaguchi-2019-compact
%X Character-level translation has been proved to be able to achieve preferable translation quality without explicit segmentation, but training a character-level model needs a lot of hardware resources. In this paper, we introduced two character-level translation models which are mid-gated model and multi-attention model for Japanese-English translation. We showed that the mid-gated model achieved the better performance with respect to BLEU scores. We also showed that a relatively narrow beam of width 4 or 5 was sufficient for the mid-gated model. As for unknown words, we showed that the mid-gated model could somehow translate the one containing Katakana by coining out a close word. We also showed that the model managed to produce tolerable results for heavily noised sentences, even though the model was trained with the dataset without noise.
%R 10.18653/v1/D19-5202
%U https://aclanthology.org/D19-5202
%U https://doi.org/10.18653/v1/D19-5202
%P 36-44

Markdown (Informal)

[Compact and Robust Models for Japanese-English Character-level Machine Translation](https://aclanthology.org/D19-5202) (Dai & Yamaguchi, WAT 2019)

• Compact and Robust Models for Japanese-English Character-level Machine Translation (Dai & Yamaguchi, WAT 2019)

ACL

• Jinan Dai and Kazunori Yamaguchi. 2019. Compact and Robust Models for Japanese-English Character-level Machine Translation. In Proceedings of the 6th Workshop on Asian Translation, pages 36–44, Hong Kong, China. Association for Computational Linguistics.