Robust Incremental Neural Semantic Graph Parsing

Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pages 1215–1226 Vancouver, Canada, July 30 - August 4, 2017. c⃝2017 Association for Computational Linguistics https://doi.org/10.18653/v1/P17-1112 Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pages 1215–1226 Vancouver, Canada, July 30 - August 4, 2017. c⃝2017 Association for Computational Linguistics https://doi.org/10.18653/v1/P17-1112 Robust Incremental Neural Semantic Graph Parsing Jan Buys1 and Phil Blunsom1,2 1Department of Computer Science, University of Oxford 2DeepMind {jan.buys,phil.blunsom}@cs.ox.ac.uk Abstract Parsing sentences to linguistically- expressive semantic representations is a key goal of Natural Language Process- ing. Yet statistical parsing has focussed almost exclusively on bilexical depen- dencies 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. 1 Introduction An important goal of Natural Language Under- standing (NLU) is to parse sentences to structured, interpretable meaning representations that can be used for query execution, inference and reasoning. Recently end-to-end models have outperformed traditional pipeline approaches, predicting syntac- tic or semantic structure as intermediate steps, on NLU tasks such as sentiment analysis and seman- tic relatedness (Le and Mikolov, 2014; Kiros et al., 2015), question answering (Hermann et al., 2015) and textual entailment (Rockt¨aschel et al., 2015). However the linguistic structure used in applica- tions has predominantly been shallow, restricted to bilexical dependencies or trees. In this paper we focus on robust parsing into linguistically deep representations. The main rep- resentation that we use is Minimal Recursion Se- mantics (MRS) (Copestake et al., 1995, 2005), which serves as the semantic representation of the English Resource Grammar (ERG) (Flickinger, 2000). Existing parsers for full MRS (as op- posed to bilexical semantic graphs derived from, but simplifying MRS) are grammar-based, per- forming disambiguation with a maximum entropy model (Toutanova et al., 2005; Zhang et al., 2007); this approach has high precision but incomplete coverage. Our main contribution is to develop a fast and robust parser for full MRS-based semantic graphs. We exploit the power of global conditioning en- abled by deep learning to predict linguistically deep graphs incrementally. The model does not have access to the underlying ERG or syntac- tic structures from which the MRS analyses were originally derived. We develop parsers for two graph-based conversions of MRS, Elementary De- pendency Structure (EDS) (Oepen and Lønning, 2006) and Dependency MRS (DMRS) (Copes- take, 2009), of which the latter is inter-convertible with MRS. Abstract Meaning Representation (AMR) (Ba- narescu et al., 2013) is a graph-based semantic representation that shares the goals of MRS. Aside …

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