A Framework for Interactive and Automatic Refinement of Transfer-based Machine Translation

Most current Machine Translation (MT) systems do not improve with feedback from post-editors beyond the addition of corrected translations to parallel training data (for statistical and example-base MT) or to a memory database. Rule based systems to date improve only via manual debugging. In contrast, we propose a largely automated method for capturing more information from human post-editors, so that corrections may be performed automatically to translation grammar rules and lexical entries. This paper introduces a general framework for incorporating a refinement module into rule-based transfer MT systems. This framework allows for generalizing post-editing efforts in an effective way, by identifying and correcting rules semi-automatically on order to improve coverage and overall translation quality.