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.- Anthology ID:
- 2020.aacl-main.12
- Volume:
- 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:
- December
- Year:
- 2020
- Address:
- Suzhou, China
- Editors:
- Kam-Fai Wong, Kevin Knight, Hua Wu
- Venue:
- AACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 93–99
- Language:
- URL:
- https://aclanthology.org/2020.aacl-main.12
- DOI:
- Cite (ACL):
- Xinyu Wang and Kewei Tu. 2020. Second-Order Neural Dependency Parsing with Message Passing and End-to-End Training. In 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, pages 93–99, Suzhou, China. Association for Computational Linguistics.
- Cite (Informal):
- Second-Order Neural Dependency Parsing with Message Passing and End-to-End Training (Wang & Tu, AACL 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2020.aacl-main.12.pdf
- Code
- wangxinyu0922/Second_Order_Parsing
- Data
- Chinese Treebank, Penn Treebank