Response-based Learning for Grounded Machine Translation

Stefan Riezler, Patrick Simianer, Carolin Haas

Abstract: We propose a novel learning approach for statistical machine translation (SMT) that allows to extract supervision signals for structured learning from an extrinsic response to a translation input. We show how to generate responses by grounding SMT in the task of executing a semantic parse of a translated query against a database. Experiments on the GEOQUERY database show an improvement of about 6 points in F1-score for responsebased learning over learning from references only on returning the correct answer from a semantic parse of a translated query. In general, our approach alleviates the dependency on human reference translations and solves the reachability problem in structured learning for SMT.