Second-Order Semantic Dependency Parsing with End-to-End Neural Networks
Xinyu Wang, Jingxian Huang, Kewei Tu · 2019
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 stateof-the-art performance.