  Few-Shot Relation Extraction aims at predicting the relation for a pair of
entities in a sentence by training with a few labelled examples in each
relation. Some recent works have introduced relation information (i.e.,
relation labels or descriptions) to assist model learning based on Prototype
Network. However, most of them constrain the prototypes of each relation class
implicitly with relation information, generally through designing complex
network structures, like generating hybrid features, combining with contrastive
learning or attention networks. We argue that relation information can be
introduced more explicitly and effectively into the model. Thus, this paper
proposes a direct addition approach to introduce relation information.
Specifically, for each relation class, the relation representation is first
generated by concatenating two views of relations (i.e., [CLS] token embedding
and the mean value of embeddings of all tokens) and then directly added to the
original prototype for both train and prediction. Experimental results on the
benchmark dataset FewRel 1.0 show significant improvements and achieve
comparable results to the state-of-the-art, which demonstrates the
effectiveness of our proposed approach. Besides, further analyses verify that
the direct addition is a much more effective way to integrate the relation
representations and the original prototypes.
