Graph Neural Networks with Generated Parameters for Relation Extraction

Hao Zhu, Yankai Lin, Zhiyuan Liu, Jie Fu, Tat-Seng Chua, Maosong Sun


Abstract
In this paper, we propose a novel graph neural network with generated parameters (GP-GNNs). The parameters in the propagation module, i.e. the transition matrices used in message passing procedure, are produced by a generator taking natural language sentences as inputs. We verify GP-GNNs in relation extraction from text, both on bag- and instance-settings. Experimental results on a human-annotated dataset and two distantly supervised datasets show that multi-hop reasoning mechanism yields significant improvements. We also perform a qualitative analysis to demonstrate that our model could discover more accurate relations by multi-hop relational reasoning.
Anthology ID:
P19-1128
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Editors:
Anna Korhonen, David Traum, Lluís Màrquez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1331–1339
Language:
URL:
https://aclanthology.org/P19-1128
DOI:
10.18653/v1/P19-1128
Bibkey:
Cite (ACL):
Hao Zhu, Yankai Lin, Zhiyuan Liu, Jie Fu, Tat-Seng Chua, and Maosong Sun. 2019. Graph Neural Networks with Generated Parameters for Relation Extraction. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1331–1339, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Graph Neural Networks with Generated Parameters for Relation Extraction (Zhu et al., ACL 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/P19-1128.pdf