Probabilistic Formalization for Example-based Machine Translation

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  • ARAMAKI EIJI
    Department of Planning, Information and Management, University of Tokyo Hospital
  • KUROHASHI SADAO
    Graduate School of Informatics, Kyoto University
  • KASHIOKA HIDEKI
    National Institute of Information and Communications Technology ATR Spoken Language Translation Research Laboratories
  • KATO NAOTO
    Science and Technical Research Laboratories of NHK

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Other Title
  • 用例ベース翻訳の確率的モデル化
  • ヨウレイ ベース ホンヤク ノ カクリツテキ モデルカ

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Abstract

Example-based machine translation (EBMT) systems, so far, rely on heuristic measures in retrieving translation examples.Such a heuristic measure costs time to adjust, and might make its algorithm unclear.This paper presents a probabilistic model for EBMT.Under the proposed model, the system searches the translation example combination which has the highest probability.The proposed model clearly formalizes EBMT process.In addition, the model can naturally incorporate the context similarity of translation examples.The experimental results demonstrate that the proposed model has a slightly better translation quality than state-of-the-art EBMT systems.

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