@inproceedings{wang-tu-2020-second,
title = "Second-Order Neural Dependency Parsing with Message Passing and End-to-End Training",
author = "Wang, Xinyu and
Tu, Kewei",
editor = "Wong, Kam-Fai and
Knight, Kevin and
Wu, Hua",
booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.aacl-main.12/",
doi = "10.18653/v1/2020.aacl-main.12",
pages = "93--99",
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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%0 Conference Proceedings
%T Second-Order Neural Dependency Parsing with Message Passing and End-to-End Training
%A Wang, Xinyu
%A Tu, Kewei
%Y Wong, Kam-Fai
%Y Knight, Kevin
%Y Wu, Hua
%S Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing
%D 2020
%8 December
%I Association for Computational Linguistics
%C Suzhou, China
%F wang-tu-2020-second
%X 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.
%R 10.18653/v1/2020.aacl-main.12
%U https://aclanthology.org/2020.aacl-main.12/
%U https://doi.org/10.18653/v1/2020.aacl-main.12
%P 93-99
Markdown (Informal)
[Second-Order Neural Dependency Parsing with Message Passing and End-to-End Training](https://aclanthology.org/2020.aacl-main.12/) (Wang & Tu, AACL 2020)
ACL