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

ACL 2019  ·  Xinyu Wang, Jingxian Huang, Kewei Tu ·

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.

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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Semantic Dependency Parsing DM MFVI In-domain 94.0 # 3
Out-of-domain 89.7 # 3
Semantic Dependency Parsing PAS MFVI In-domain 94.1 # 3
Out-of-domain 91.3 # 3
Semantic Dependency Parsing PSD MFVI In-domain 81.4 # 3
Out-of-domain 79.6 # 3

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