ConstGCN: Constrained Transmission-based Graph Convolutional Networks for Document-level Relation Extraction

• URL: http://arxiv.org/abs/2210.03949v1
• Date: Sat, 8 Oct 2022 07:36:04 GMT
• Title: ConstGCN: Constrained Transmission-based Graph Convolutional Networks for Document-level Relation Extraction
• Authors: Ji Qi, Bin Xu, Kaisheng Zeng, Jinxin Liu, Jifan Yu, Qi Gao, Juanzi Li, Lei Hou
• Abstract summary: Document-level relation extraction with graph neural networks faces a fundamental graph construction gap between training and inference. We propose $textbfConstGCN$, a novel graph convolutional network which performs knowledge-based information propagation between entities. Experimental results show that our method outperforms the previous state-of-the-art (SOTA) approaches on the DocRE dataset.
• Score: 24.970508961370548
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Document-level relation extraction with graph neural networks faces a fundamental graph construction gap between training and inference - the golden graph structure only available during training, which causes that most methods adopt heuristic or syntactic rules to construct a prior graph as a pseudo proxy. In this paper, we propose $\textbf{ConstGCN}$, a novel graph convolutional network which performs knowledge-based information propagation between entities along with all specific relation spaces without any prior graph construction. Specifically, it updates the entity representation by aggregating information from all other entities along with each relation space, thus modeling the relation-aware spatial information. To control the information flow passing through the indeterminate relation spaces, we propose to constrain the propagation using transmitting scores learned from the Noise Contrastive Estimation between fact triples. Experimental results show that our method outperforms the previous state-of-the-art (SOTA) approaches on the DocRE dataset.

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