Exploring Entity Interactions for Few-Shot Relation Learning (Student Abstract). (arXiv:2205.01878v1 [cs.CL])

Web: http://arxiv.org/abs/2205.01878

Few-shot relation learning refers to infer facts for relations with a limited
number of observed triples. Existing metric-learning methods for this problem
mostly neglect entity interactions within and between triples. In this paper,
we explore this kind of fine-grained semantic meanings 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
both intra- and inter-triple entity interactions. Experiments on two public
benchmark datasets NELL-One and Wiki-One …

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