tailieunhanh - Báo cáo khoa học: "Feedback Cleaning of Machine Translation Rules Using Automatic Evaluation"

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. | Feedback Cleaning of Machine Translation Rules Using Automatic Evaluation Kenji Imamura Eiichiro Sumita ATR Spoken Language Translation Research Laboratories Seika-cho Soraku-gun Kyoto Japan @ Yuji Matsumoto Nara Institute of Science and Technology Ikoma-shi Nara Japan matsu@ Abstract When rules of transfer-based machine translation MT are automatically acquired from bilingual corpora incor-rect 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 incor-rect redundant rules as a way to increase the evaluation score. BLEU is utilized for the automatic evaluation. The hillclimbing 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. 1 Introduction Along with the efforts made in accumulating bilingual corpora for many language pairs quite a few machine translation MT systems that automatically acquire their knowledge from corpora have been proposed. However knowledge for transferbased MT acquired from corpora contains many in-correct redundant rules due to acquisition errors or translation variety in the corpora. Such rules conflict with other existing rules and cause implausible MT results or increase ambiguity. If incorrect rules could be avoided MT quality would necessarily improve. There are two approaches to overcoming incor-rect redundant rules Selecting appropriate rules in a disambiguation process during the translation on-line processing Meyers et al. 2000 . Cleaning incorrect redundant rules after automatic acquisition off-line processing Menezes and Richardson 2001 Imamura 2002 . We employ the second approach