  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.
