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

2020-10-10 13:49:46
Xinyu Wang, Kewei Tu
arXiv_CL
       
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.

Abstract (translated)

URL

https://arxiv.org/abs/2010.05003

PDF

https://arxiv.org/pdf/2010.05003.pdf

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