Paper: Feedback Cleaning Of Machine Translation Rules Using Automatic Evaluation

ACL ID P03-1057
Title Feedback Cleaning Of Machine Translation Rules Using Automatic Evaluation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2003
Authors

When rules of transfer-based machine translation (MT) are automatically ac- quired 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 evalua- tion 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 hill- climbing algorithm, which involves fea- tures of this task, is applied to searching for the optimal combination of rules. Our experiments show that the MT quality im- proves by 10% in test sentences according to a subjective evaluation. This is consid- erable improvement over previous meth- ods.