Feedback cleaning of machine translation rules using automatic evaluation

Authors:
Kenji Imamura;Eiichiro Sumita;Yuji Matsumoto
Affiliations:
ATR Spoken Language Translation Research Laboratories, Kyoto, Japan;ATR Spoken Language Translation Research Laboratories, Kyoto, Japan;Nara Institute of Science and Technology, Nara, Japan
Venue:
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Year:
2003

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Abstract

When rules of transfer-based machine translation (MT) are automatically acquired from bilingual corpora, incorrect/redundant rules are generated due to acquisition errors or translation variety in the corpora. As a new countermeasure to this problem, we propose a feedback cleaning method using automatic evaluation of MT quality, which removes incorrect/redundant rules as a way to increase the evaluation score. BLEU is utilized for the automatic evaluation. The hill-climbing algorithm, which involves features of this task, is applied to searching for the optimal combination of rules. Our experiments show that the MT quality improves by 10% in test sentences according to a subjective evaluation. This is considerable improvement over previous methods.