Robust Incremental Neural Semantic Graph Parsing
Jan Buys, Phil Blunsom · 2017
Parsing sentences to linguisticallyexpressive semantic representations is a key goal of Natural Language Processing.Yet statistical parsing has focussed almost exclusively on bilexical dependencies or domain-specific logical forms.We propose a neural encoder-decoder transition-based parser which is the first full-coverage semantic graph parser for Minimal Recursion Semantics (MRS).The model architecture uses stack-based embedding features, predicting graphs jointly with unlexicalized predicates and their token alignments.Our parser is more accurate than attention-based baselines on MRS, and on an additional Abstract Meaning Representation (AMR) benchmark, and GPU batch processing makes it an order of magnitude faster than a high-precision grammar-based parser.Further, the 86.69%Smatch score of our MRS parser is higher than the upper-bound on AMR parsing, making MRS an attractive choice as a semantic representation.