Second-Order Neural Dependency Parsing with Message Passing and End-to-End Training

Xinyu Wang; Kewei Tu

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arXiv · 2010.05003

Second-Order Neural Dependency Parsing with Message Passing and End-to-End Training

Xinyu Wang·Kewei Tu

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Abstract

In this paper, we propose second-order graph-based neural dependency parsing using message passing and end-to-end neural networks. We empirically show that our approaches match the accuracy of very recent state-of-the-art second-order graph-based neural dependency parsers and have significantly faster speed in both training and testing. We also empirically show the advantage of second-order parsing over first-order parsing and observe that the usefulness of the head-selection structured constraint vanishes when using BERT embedding.

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Xinyu Wang, Kewei Tu. 2020-10-10. Second-Order Neural Dependency Parsing with Message Passing and End-to-End Training. https://arxiv.org/abs/2010.05003

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