A Novel Few-Shot Relation Extraction Pipeline Based on Adaptive Prototype Fusion

• URL: http://arxiv.org/abs/2210.08242v1
• Date: Sat, 15 Oct 2022 09:44:21 GMT
• Title: A Novel Few-Shot Relation Extraction Pipeline Based on Adaptive Prototype Fusion
• Authors: Yuzhe Zhang, Min Cen, Tongzhou Wu, and Hong Zhang
• Abstract summary: Few-shot relation extraction (FSRE) aims at recognizing unseen relations by learning with merely a handful of annotated instances. This paper proposes a novel pipeline for the FSRE task based on adaptive prototype fusion. Experiments on the benchmark dataset FewRel 1.0 show a significant improvement of our method against state-of-the-art methods.
• Score: 5.636675879040131
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Few-shot relation extraction (FSRE) aims at recognizing unseen relations by learning with merely a handful of annotated instances. To more effectively generalize to new relations, this paper proposes a novel pipeline for the FSRE task based on adaptive prototype fusion. Specifically, for each relation class, the pipeline fully explores the relation information by concatenating two types of embedding, and then elaborately combine the relation representation with the adaptive prototype fusion mechanism. The whole framework can be effectively and efficiently optimized in an end-to-end fashion. Experiments on the benchmark dataset FewRel 1.0 show a significant improvement of our method against state-of-the-art methods.

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