We propose a framework for improving output quality of machine translation systems, by operating on the level of grammar rule features. Our framework aims to give a boost to grammar rules that appear in the derivations of translation candidates that are deemed to be of good quality, hence making those rules more preferable by the system. To that end, we ask human annotators on Amazon Mechanical Turk to compare translation candidates, and then interpret their preferences of one candidate over another as an implicit preference for one derivation over another, and therefore as an implicit preference for one or more grammar rules. Our framework also allows us to generalize these preferences to grammar rules corresponding to a previously unseen test set, namely rules for which no candidates have been judged.
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.