Example-based machine translation based on syntactic transfer with statistical models

Authors:
Kenji Imamura;Hideo Okuma;Taro Watanabe;Eiichiro Sumita
Affiliations:
ATR Spoken Language, Translation Research Laboratories, Kyoto, Japan;ATR Spoken Language, Translation Research Laboratories, Kyoto, Japan;ATR Spoken Language, Translation Research Laboratories, Kyoto, Japan;ATR Spoken Language, Translation Research Laboratories, Kyoto, Japan
Venue:
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Year:
2004

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Abstract

This paper presents example-based machine translation (MT) based on syntactic transfer, which selects the best translation by using models of statistical machine translation. Example-based MT sometimes generates invalid translations because it selects similar examples to the input sentence based only on source language similarity. The method proposed in this paper selects the best translation by using a language model and a translation model in the same manner as statistical MT, and it can improve MT quality over that of 'pure' example-based MT. A feature of this method is that the statistical models are applied after word re-ordering is achieved by syntactic transfer. This implies that MT quality is maintained even when we only apply a lexicon model as the translation model. In addition, translation speed is improved by bottom-up generation, which utilizes the tree structure that is output from the syntactic transfer.