Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification
This work addresses few-shot learning for relation classification, an incremental improvement over existing prototypical networks.
The paper tackles few-shot relation classification by introducing a multi-level matching and aggregation network (MLMAN) that encodes query and support sets interactively, achieving state-of-the-art performance on the FewRel dataset.
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.