Machine translation systems should improve with feedback from post-editors, but none do beyond the very localized benefit of adding the corrected translation to parallel training data (for statistical and example-base MTS) or a memory data base. Rule based systems to date improve only via manual debugging. In contrast, we introduce a largely automated method for capturing more information from the human post-editor, so that corrections may be performed automatically to translation grammar rules and lexical entries. This paper focuses on the information capture phase and reports on an experiment with English-Spanish translation.
Available at: http://works.bepress.com/jaime_carbonell/182/