Robust Incremental Neural Semantic Graph Parsing

Jan Buys, Phil Blunsom


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.
Anthology ID:
P17-1112
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1215–1226
Language:
URL:
https://aclanthology.org/P17-1112
DOI:
10.18653/v1/P17-1112
Bibkey:
Cite (ACL):
Jan Buys and Phil Blunsom. 2017. Robust Incremental Neural Semantic Graph Parsing. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1215–1226, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Robust Incremental Neural Semantic Graph Parsing (Buys & Blunsom, ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-1112.pdf
Code
 janmbuys/DeepDeepParser