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        <title>Translation Using JAPIO Patent Corpora: JAPIO at WAT2016</title>
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        <namePart type="given">Terumasa</namePart>
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            <title>Proceedings of the 3rd Workshop on Asian Translation (WAT2016)</title>
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            <namePart type="given">Toshiaki</namePart>
            <namePart type="family">Nakazawa</namePart>
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            <namePart type="given">Hideya</namePart>
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            <namePart type="given">Chenchen</namePart>
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    <abstract>We participate in scientific paper subtask (ASPEC-EJ/CJ) and patent subtask (JPC-EJ/CJ/KJ) with phrase-based SMT systems which are trained with its own patent corpora. Using larger corpora than those prepared by the workshop organizer, we achieved higher BLEU scores than most participants in EJ and CJ translations of patent subtask, but in crowdsourcing evaluation, our EJ translation, which is best in all automatic evaluations, received a very poor score. In scientific paper subtask, our translations are given lower scores than most translations that are produced by translation engines trained with the in-domain corpora. But our scores are higher than those of general-purpose RBMTs and online services. Considering the result of crowdsourcing evaluation, it shows a possibility that CJ SMT system trained with a large patent corpus translates non-patent technical documents at a practical level.</abstract>
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%0 Conference Proceedings
%T Translation Using JAPIO Patent Corpora: JAPIO at WAT2016
%A Kinoshita, Satoshi
%A Oshio, Tadaaki
%A Mitsuhashi, Tomoharu
%A Ehara, Terumasa
%Y Nakazawa, Toshiaki
%Y Mino, Hideya
%Y Ding, Chenchen
%Y Goto, Isao
%Y Neubig, Graham
%Y Kurohashi, Sadao
%Y Riza, Ir. Hammam
%Y Bhattacharyya, Pushpak
%S Proceedings of the 3rd Workshop on Asian Translation (WAT2016)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F kinoshita-etal-2016-translation
%X We participate in scientific paper subtask (ASPEC-EJ/CJ) and patent subtask (JPC-EJ/CJ/KJ) with phrase-based SMT systems which are trained with its own patent corpora. Using larger corpora than those prepared by the workshop organizer, we achieved higher BLEU scores than most participants in EJ and CJ translations of patent subtask, but in crowdsourcing evaluation, our EJ translation, which is best in all automatic evaluations, received a very poor score. In scientific paper subtask, our translations are given lower scores than most translations that are produced by translation engines trained with the in-domain corpora. But our scores are higher than those of general-purpose RBMTs and online services. Considering the result of crowdsourcing evaluation, it shows a possibility that CJ SMT system trained with a large patent corpus translates non-patent technical documents at a practical level.
%U https://aclanthology.org/W16-4612
%P 133-138

Markdown (Informal)

[Translation Using JAPIO Patent Corpora: JAPIO at WAT2016](https://aclanthology.org/W16-4612) (Kinoshita et al., WAT 2016)

• Translation Using JAPIO Patent Corpora: JAPIO at WAT2016 (Kinoshita et al., WAT 2016)

ACL

• Satoshi Kinoshita, Tadaaki Oshio, Tomoharu Mitsuhashi, and Terumasa Ehara. 2016. Translation Using JAPIO Patent Corpora: JAPIO at WAT2016. In Proceedings of the 3rd Workshop on Asian Translation (WAT2016), pages 133–138, Osaka, Japan. The COLING 2016 Organizing Committee.