Exploring Entity Interactions for Few-Shot Relation Learning (Student Abstract)
Yi Liang, Shuai Zhao, Bo Cheng, Yuwei Yin, Hao Yang
[AAAI-22] Student Abstract and Poster Program - FINALIST
Abstract:
Few-shot relation learning refers to infer facts for relations with a few observed triples. Existing metric-learning methods mostly neglect entity interactions within and between triples. In this paper, we explore this kind of fine-grained semantic meaning and propose our model TransAM. Specifically, we serialize reference entities and query entities into sequence and apply transformer structure with local-global attention to capture intra- and inter-triple entity interactions. Experiments on two public datasets with 1-shot setting prove the effectiveness of TransAM.
Introduction Video
Sessions where this paper appears
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Poster Session 3
Blue 5
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Poster Session 7
Blue 5
