Xiaobo Shu


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From What to Why: Improving Relation Extraction with Rationale Graph
Zhenyu Zhang | Bowen Yu | Xiaobo Shu | Xue Mengge | Tingwen Liu | Li Guo
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


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Document-level Relation Extraction with Dual-tier Heterogeneous Graph
Zhenyu Zhang | Bowen Yu | Xiaobo Shu | Tingwen Liu | Hengzhu Tang | Wang Yubin | Li Guo
Proceedings of the 28th International Conference on Computational Linguistics

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.