Granularity-aware Area Prototypical Network with Bimargin Loss for Few Shot Relation Classification

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)4852-4866
Number of pages15
Journal / PublicationIEEE Transactions on Knowledge and Data Engineering
Issue number5
Online published1 Feb 2022
Publication statusPublished - May 2023


Relation Classification is one of the most important tasks in text mining. Previous methods either require large-scale manually-annotated data or rely on distant supervision approaches which suffer from the long-tail problem. To reduce the expensive manually-annotating cost and solve the long-tail problem, prototypical networks are widely used in few-shot RC tasks. Despite their remarkable performance, current prototypical networks ignore the different granularities of relations, which degrades the classification performance dramatically. Moreover, the optimization of current prototypical networks simply relies on the cross-entropy loss, which cannot consider the intra-relation compactness and the dispersion among relations in a semantic space. It is not robust enough for current prototypical network in real-world and complicated scenarios. In this paper, we propose an area prototypical network with a granularity-aware measurement, aiming to considering the different granularities of relations. Each relation is represented as an area whose width can reflect the granularity level of relation. Moreover, to improve the robustness, bimargin loss is designed to force area prototypical network to improve the intra-relation compactness and inter-relation dispersion for the feature representation in a semantic space. Extensive experiments on two public datasets are conducted and evaluate the effectiveness of our proposed model.

Research Area(s)

  • Computational modeling, Data models, Few-shot Learning, Metric Learning, Prototypes, Prototypical Network, Relation Classification, Robustness, Semantics, Task analysis, Training

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