Paper: Learning For Semantic Parsing With Statistical Machine Translation

ACL ID N06-1056
Title Learning For Semantic Parsing With Statistical Machine Translation
Venue Human Language Technologies
Session Main Conference
Year 2006
Authors

We present a novel statistical approach to semantic parsing, WASP, for construct- ing a complete, formal meaning represen- tation of a sentence. A semantic parser is learned given a set of sentences anno- tated with their correct meaning represen- tations. The main innovation of WASP is its use of state-of-the-art statistical ma- chine translation techniques. A word alignment model is used for lexical acqui- sition, and the parsing model itself can be seen as a syntax-based translation model. We show that WASP performs favorably in terms of both accuracy and coverage compared to existing learning methods re- quiringsimilaramountofsupervision,and shows better robustness to variations in task complexity and word order.