Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification

Zhi-Xiu Ye; Zhen-Hua Ling

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arXiv · 1906.06678

Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification

Zhi-Xiu Ye·Zhen-Hua Ling

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Abstract

This paper presents a multi-level matching and aggregation network (MLMAN) for few-shot relation classification. Previous studies on this topic adopt prototypical networks, which calculate the embedding vector of a query instance and the prototype vector of each support set independently. In contrast, our proposed MLMAN model encodes the query instance and each support set in an interactive way by considering their matching information at both local and instance levels. The final class prototype for each support set is obtained by attentive aggregation over the representations of its support instances, where the weights are calculated using the query instance. Experimental results demonstrate the effectiveness of our proposed methods, which achieve a new state-of-the-art performance on the FewRel dataset.

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Zhi-Xiu Ye, Zhen-Hua Ling. 2019-06-16. Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification. https://arxiv.org/abs/1906.06678

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