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        <title>Graph Neural Networks with Generated Parameters for Relation Extraction</title>
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        <namePart type="given">Hao</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Graph Neural Networks with Generated Parameters for Relation Extraction
%A Zhu, Hao
%A Lin, Yankai
%A Liu, Zhiyuan
%A Fu, Jie
%A Chua, Tat-Seng
%A Sun, Maosong
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F zhu-etal-2019-graph
%X 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.
%R 10.18653/v1/P19-1128
%U https://aclanthology.org/P19-1128
%U https://doi.org/10.18653/v1/P19-1128
%P 1331-1339

Markdown (Informal)

[Graph Neural Networks with Generated Parameters for Relation Extraction](https://aclanthology.org/P19-1128) (Zhu et al., ACL 2019)

• Graph Neural Networks with Generated Parameters for Relation Extraction (Zhu et al., ACL 2019)

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.