Second-Order Semantic Dependency Parsing with End-to-End Neural Networks

Xinyu Wang, Jingxian Huang, Kewei Tu

2019-06-19ACL 2019 7Dependency Parsing Variational Inference

Abstract

Semantic dependency parsing aims to identify semantic relationships between words in a sentence that form a graph. In this paper, we propose a second-order semantic dependency parser, which takes into consideration not only individual dependency edges but also interactions between pairs of edges. We show that second-order parsing can be approximated using mean field (MF) variational inference or loopy belief propagation (LBP). We can unfold both algorithms as recurrent layers of a neural network and therefore can train the parser in an end-to-end manner. Our experiments show that our approach achieves state-of-the-art performance.

Results

Task	Dataset	Metric	Value	Model
Semantic Parsing	DM	In-domain	94	MFVI
Semantic Parsing	DM	Out-of-domain	89.7	MFVI
Semantic Parsing	PSD	In-domain	81.4	MFVI
Semantic Parsing	PSD	Out-of-domain	79.6	MFVI
Semantic Parsing	PAS	In-domain	94.1	MFVI
Semantic Parsing	PAS	Out-of-domain	91.3	MFVI

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