Broad-coverage CCG Semantic Parsing with AMR
Yoav Artzia b, Kenton Leeb c, Luke Zettlemoyerb c
- aDept. of Computer Science and Cornell Tech Cornell University New York , NY 10011
- bUniversity of Washington
- cComputer Science & Engineering University of Washington Seattle , WA 98195
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Abstract
We propose a grammar induction technique for AMR semantic parsing.While previous grammar induction techniques were designed to re-learn a new parser for each target application, the recently annotated AMR Bank provides a unique opportunity to induce a single model for understanding broad-coverage newswire text and support a wide range of applications.We present a new model that combines CCG parsing to recover compositional aspects of meaning and a factor graph to model non-compositional phenomena, such as anaphoric dependencies.Our approach achieves 66.2 Smatch F1 score on the AMR bank, significantly outperforming the previous state of the art.