Abstract
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 state-of-the-art performance.- Anthology ID:
- P19-1454
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4609–4618
- Language:
- URL:
- https://aclanthology.org/P19-1454
- DOI:
- 10.18653/v1/P19-1454
- Cite (ACL):
- Xinyu Wang, Jingxian Huang, and Kewei Tu. 2019. Second-Order Semantic Dependency Parsing with End-to-End Neural Networks. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4609–4618, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Second-Order Semantic Dependency Parsing with End-to-End Neural Networks (Wang et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/P19-1454.pdf
- Code
- wangxinyu0922/Second_Order_SDP + additional community code