Paper: Response-based Learning for Grounded Machine Translation

ACL ID P14-1083
Title Response-based Learning for Grounded Machine Translation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014

We propose a novel learning approach for statistical machine translation (SMT) that allows to extract supervision signals for structured learning from an extrinsic re- sponse to a translation input. We show how to generate responses by grounding SMT in the task of executing a seman- tic parse of a translated query against a database. Experiments on the GEO- QUERY database show an improvement of about 6 points in F1-score for response- based learning over learning from refer- ences only on returning the correct an- swer from a semantic parse of a translated query. In general, our approach alleviates the dependency on human reference trans- lations and solves the reachability problem in structured learning for SMT.