Abstract
Parsing sentences to linguistically-expressive 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.
Highlights
An important goal of Natural Language Understanding (NLU) is to parse sentences to structured, interpretable meaning representations that can be used for query execution, inference and reasoning
We develop parsers for two graph-based conversions of Minimal Recursion Semantics (MRS), Elementary Dependency Structure (EDS) (Oepen and Lønning, 2006) and Dependency MRS (DMRS) (Copestake, 2009), of which the latter is inter-convertible with MRS
The Smatch metric (Cai and Knight, 2013), proposed for evaluating Abstract Meaning Representation (AMR) graphs, measures graph overlap, but does not rely on sentence alignments to determine the correspondences between graph nodes
Summary
An important goal of Natural Language Understanding (NLU) is to parse sentences to structured, interpretable meaning representations that can be used for query execution, inference and reasoning. End-to-end models have outperformed traditional pipeline approaches, predicting syntactic or semantic structure as intermediate steps, on NLU tasks such as sentiment analysis and semantic relatedness (Le and Mikolov, 2014; Kiros et al, 2015), question answering (Hermann et al, 2015) and textual entailment (Rocktaschel et al, 2015). In this paper we focus on robust parsing into linguistically deep representations. Existing parsers for full MRS (as opposed to bilexical semantic graphs derived from, but simplifying MRS) are grammar-based, performing disambiguation with a maximum entropy model (Toutanova et al, 2005; Zhang et al, 2007); this approach has high precision but incomplete coverage
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