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Utilization of Multi-word Expressions to Improve Statistical Machine Translation of Statutory Sentences

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New Frontiers in Artificial Intelligence (JSAI-isAI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10091))

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Abstract

Statutory sentences are generally difficult to read because of their complicated expressions and length. Such difficulty is one reason for the low quality of statistical machine translation (SMT). Multi-word expressions (MWEs) also complicate statutory sentences and extend their length. Therefore, we proposed a method that utilizes MWEs to improve the SMT system of statutory sentences. In our method, we extracted the monolingual MWEs from a parallel corpus, automatically acquired these translations based on the Dice coefficient, and integrated the extracted bilingual MWEs into an SMT system by the single-tokenization strategy. The experiment results with our SMT system using the proposed method significantly improved the translation quality. Although automatic translation equivalent acquisition using the Dice coefficient is not perfect, the best system’s score was close to a system that used bilingual MWEs whose equivalents are translated by hand.

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Notes

  1. 1.

    http://www.japaneselawtranslation.go.jp/.

  2. 2.

    https://github.com/yohasebe/lemmatizer/.

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Acknowledgements

This research was partly supported by the Japan Society for the Promotion of Science KAKENHI Grant-in-Aid for Scientific Research (S) No. 23220005, (A) No. 26240050 and (C) No. 15K00201.

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Authors and Affiliations

  1. Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan

    Satomi Sakamoto, Yasuhiro Ogawa, Tomohiro Ohno & Katsuhiko Toyama

  2. Information Technology Center, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan

    Yasuhiro Ogawa, Tomohiro Ohno & Katsuhiko Toyama

  3. Graduate School of Law, Japan Legal Information Institute, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan

    Makoto Nakamura

Authors
  1. Satomi Sakamoto
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  2. Yasuhiro Ogawa
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  3. Makoto Nakamura
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  4. Tomohiro Ohno
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  5. Katsuhiko Toyama
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Corresponding author

Correspondence to Yasuhiro Ogawa .

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Editors and Affiliations

  1. Chiba University Graduate School of Engineering, Chiba, Japan

    Mihoko Otake

  2. University of Tsukuba, Tokyo, Japan

    Setsuya Kurahashi

  3. Fujitsu Laboratories, Kanagawa, Japan

    Yuiko Ota

  4. National Institute of Informatics, Tokyo, Japan

    Ken Satoh

  5. Ochanomizu University, Tokyo, Japan

    Daisuke Bekki

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Sakamoto, S., Ogawa, Y., Nakamura, M., Ohno, T., Toyama, K. (2017). Utilization of Multi-word Expressions to Improve Statistical Machine Translation of Statutory Sentences. In: Otake, M., Kurahashi, S., Ota, Y., Satoh, K., Bekki, D. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2015. Lecture Notes in Computer Science(), vol 10091. Springer, Cham. https://doi.org/10.1007/978-3-319-50953-2_18

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  • DOI: https://doi.org/10.1007/978-3-319-50953-2_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50952-5

  • Online ISBN: 978-3-319-50953-2

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