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    <abstract>Document-level Relation Extraction (RE) is particularly challenging due to complex semantic interactions among multiple entities in a document. Among exiting approaches, Graph Convolutional Networks (GCN) is one of the most effective approaches for document-level RE. However, traditional GCN simply takes word nodes and adjacency matrix to represent graphs, which is difficult to establish direct connections between distant entity pairs. In this paper, we propose Global Context-enhanced Graph Convolutional Networks (GCGCN), a novel model which is composed of entities as nodes and context of entity pairs as edges between nodes to capture rich global context information of entities in a document. Two hierarchical blocks, Context-aware Attention Guided Graph Convolution (CAGGC) for partially connected graphs and Multi-head Attention Guided Graph Convolution (MAGGC) for fully connected graphs, could take progressively more global context into account. Meantime, we leverage a large-scale distantly supervised dataset to pre-train a GCGCN model with curriculum learning, which is then fine-tuned on the human-annotated dataset for further improving document-level RE performance. The experimental results on DocRED show that our model could effectively capture rich global context information in the document, leading to a state-of-the-art result. Our code is available at https://github.com/Huiweizhou/GCGCN.</abstract>
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%0 Conference Proceedings
%T Global Context-enhanced Graph Convolutional Networks for Document-level Relation Extraction
%A Zhou, Huiwei
%A Xu, Yibin
%A Yao, Weihong
%A Liu, Zhe
%A Lang, Chengkun
%A Jiang, Haibin
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F zhou-etal-2020-global
%X Document-level Relation Extraction (RE) is particularly challenging due to complex semantic interactions among multiple entities in a document. Among exiting approaches, Graph Convolutional Networks (GCN) is one of the most effective approaches for document-level RE. However, traditional GCN simply takes word nodes and adjacency matrix to represent graphs, which is difficult to establish direct connections between distant entity pairs. In this paper, we propose Global Context-enhanced Graph Convolutional Networks (GCGCN), a novel model which is composed of entities as nodes and context of entity pairs as edges between nodes to capture rich global context information of entities in a document. Two hierarchical blocks, Context-aware Attention Guided Graph Convolution (CAGGC) for partially connected graphs and Multi-head Attention Guided Graph Convolution (MAGGC) for fully connected graphs, could take progressively more global context into account. Meantime, we leverage a large-scale distantly supervised dataset to pre-train a GCGCN model with curriculum learning, which is then fine-tuned on the human-annotated dataset for further improving document-level RE performance. The experimental results on DocRED show that our model could effectively capture rich global context information in the document, leading to a state-of-the-art result. Our code is available at https://github.com/Huiweizhou/GCGCN.
%R 10.18653/v1/2020.coling-main.461
%U https://aclanthology.org/2020.coling-main.461
%U https://doi.org/10.18653/v1/2020.coling-main.461
%P 5259-5270

Markdown (Informal)

[Global Context-enhanced Graph Convolutional Networks for Document-level Relation Extraction](https://aclanthology.org/2020.coling-main.461) (Zhou et al., COLING 2020)

• Global Context-enhanced Graph Convolutional Networks for Document-level Relation Extraction (Zhou et al., COLING 2020)

ACL

• Huiwei Zhou, Yibin Xu, Weihong Yao, Zhe Liu, Chengkun Lang, and Haibin Jiang. 2020. Global Context-enhanced Graph Convolutional Networks for Document-level Relation Extraction. In Proceedings of the 28th International Conference on Computational Linguistics, pages 5259–5270, Barcelona, Spain (Online). International Committee on Computational Linguistics.