Graph-based Dependency Parsing with Graph Neural Networks

Tao Ji, Yuanbin Wu, Man Lan


Abstract
We investigate the problem of efficiently incorporating high-order features into neural graph-based dependency parsing. Instead of explicitly extracting high-order features from intermediate parse trees, we develop a more powerful dependency tree node representation which captures high-order information concisely and efficiently. We use graph neural networks (GNNs) to learn the representations and discuss several new configurations of GNN’s updating and aggregation functions. Experiments on PTB show that our parser achieves the best UAS and LAS on PTB (96.0%, 94.3%) among systems without using any external resources.
Anthology ID:
P19-1237
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:
2475–2485
Language:
URL:
https://aclanthology.org/P19-1237
DOI:
10.18653/v1/P19-1237
Bibkey:
Cite (ACL):
Tao Ji, Yuanbin Wu, and Man Lan. 2019. Graph-based Dependency Parsing with Graph Neural Networks. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2475–2485, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Graph-based Dependency Parsing with Graph Neural Networks (Ji et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1237.pdf
Code
 AntNLP/gnn-dep-parsing
Data
Penn Treebank