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Response-based learning allows to adapt a statistical machine translation (SMT) system to an extrinsic task by extracting supervision signals from task-specific feedback. In this paper, we elicit response signals for SMT adaptation by executing semantic parses of translated queries against the Freebase database. The challenge of our work lies in scaling semantic parsers to the lexical diversity of opendomain databases. We find that parser performance on incorrect English sentences, which is standardly ignored in parser evaluation, is key in model selection. In our experiments, the biggest improvements in F1-score for returning the correct answer from a semantic parse for a translated query are achieved by selecting a parser that is carefully enhanced by paraphrases and synonyms.

Response-based Learning for Machine Translation of Open-domain Database Queries