Response-based Learning for Grounded Machine Translation
Stefan Riezler, Patrick Simianer, Carolin Haas · 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. 1