Broad-coverage CCG Semantic Parsing with AMR
Yoav Artzi, Kenton Lee, Luke Zettlemoyer · 2015
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.