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    <abstract>Document-level relation extraction (RE) poses new challenges over its sentence-level counterpart since it requires an adequate comprehension of the whole document and the multi-hop reasoning ability across multiple sentences to reach the final result. In this paper, we propose a novel graph-based model with Dual-tier Heterogeneous Graph (DHG) for document-level RE. In particular, DHG is composed of a structure modeling layer followed by a relation reasoning layer. The major advantage is that it is capable of not only capturing both the sequential and structural information of documents but also mixing them together to benefit for multi-hop reasoning and final decision-making. Furthermore, we employ Graph Neural Networks (GNNs) based message propagation strategy to accumulate information on DHG. Experimental results demonstrate that the proposed method achieves state-of-the-art performance on two widely used datasets, and further analyses suggest that all the modules in our model are indispensable for document-level RE.</abstract>
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%0 Conference Proceedings
%T Document-level Relation Extraction with Dual-tier Heterogeneous Graph
%A Zhang, Zhenyu
%A Yu, Bowen
%A Shu, Xiaobo
%A Liu, Tingwen
%A Tang, Hengzhu
%A Yubin, Wang
%A Guo, Li
%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 zhang-etal-2020-document
%X Document-level relation extraction (RE) poses new challenges over its sentence-level counterpart since it requires an adequate comprehension of the whole document and the multi-hop reasoning ability across multiple sentences to reach the final result. In this paper, we propose a novel graph-based model with Dual-tier Heterogeneous Graph (DHG) for document-level RE. In particular, DHG is composed of a structure modeling layer followed by a relation reasoning layer. The major advantage is that it is capable of not only capturing both the sequential and structural information of documents but also mixing them together to benefit for multi-hop reasoning and final decision-making. Furthermore, we employ Graph Neural Networks (GNNs) based message propagation strategy to accumulate information on DHG. Experimental results demonstrate that the proposed method achieves state-of-the-art performance on two widely used datasets, and further analyses suggest that all the modules in our model are indispensable for document-level RE.
%R 10.18653/v1/2020.coling-main.143
%U https://aclanthology.org/2020.coling-main.143
%U https://doi.org/10.18653/v1/2020.coling-main.143
%P 1630-1641

Markdown (Informal)

[Document-level Relation Extraction with Dual-tier Heterogeneous Graph](https://aclanthology.org/2020.coling-main.143) (Zhang et al., COLING 2020)

• Document-level Relation Extraction with Dual-tier Heterogeneous Graph (Zhang et al., COLING 2020)

ACL

• Zhenyu Zhang, Bowen Yu, Xiaobo Shu, Tingwen Liu, Hengzhu Tang, Wang Yubin, and Li Guo. 2020. Document-level Relation Extraction with Dual-tier Heterogeneous Graph. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1630–1641, Barcelona, Spain (Online). International Committee on Computational Linguistics.