Word selection for EBMT based on monolingual similarity and translation confidence

Authors:
Eiji Aramaki;Sadao Kurohashi;Hideki Kashioka;Hideki Tanaka
Affiliations:
University of Tokyo Hongo, Bunkyo-ku, Tokyo, Japan;University of Tokyo Hongo, Bunkyo-ku, Tokyo, Japan;ATR Spoken Language Translation Research Laboratories, Kyoto, Japan;ATR Spoken Language Translation Research Laboratories, Kyoto, Japan
Venue:
HLT-NAACL-PARALLEL '03 Proceedings of the HLT-NAACL 2003 Workshop on Building and using parallel texts: data driven machine translation and beyond - Volume 3
Year:
2003

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Abstract

We propose a method of constructing an example-based machine translation (EBMT) system that exploits a content-aligned bilingual corpus. First, the sentences and phrases in the corpus are aligned across the two languages, and the pairs with high translation confidence are selected and stored in the translation memory. Then, for a given input sentences, the system searches for fitting examples based on both the monolingual similarity and the translation confidence of the pair, and the obtained results are then combined to generate the translation. Our experiments on translation selection showed the accuracy of 85% demonstrating the basic feasibility of our approach.