Abstract
Document-level relation extraction aims to extract relations between entities mentioned in the given text. Existing approaches characterize relations by concatenating the representation of entities from numerous instances for each relation. However, it fails to identify multiple relations that may be expressed by the same entity pair in few-shot scenarios, since there may be only one instance for some relations. In this paper, we propose a Context-aware Hybrid Attention Network (CHAN) for few-shot document-level relation extraction to identify multi-relation. Specifically, we design instance-specific attention to localize the relevant context for each entity pair and capture keywords associated with different relations. In addition, we introduce a contrastive prototypical network to further distinguish the subtle difference between multiple relations. Experimental results show that CHAN achieved the best performance compared to previous methods, especially the F1 of the multi-relation identification is improved by 17.94% under 1-doc setting in FREDo benchmark.
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Wang, D., Wu, S., Zhang, X., Feng, Z. (2023). Multi-relation Identification for Few-Shot Document-Level Relation Extraction. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14262. Springer, Cham. https://doi.org/10.1007/978-3-031-44201-8_5
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