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Few-shot relation extraction aims to learn to identify the relation betweentwo entities based on very limited training examples. Recent efforts found thattextual labels (i.e., relation names and relation descriptions) could beextremely useful for learning class representations, which will benefit thefew-shot learning task. However, what is the best way to leverage such labelinformation in the learning process is an important research question. Existingworks largely assume such textual labels are always present during bothlearning and prediction. In this work, we argue that such approaches may notalways lead to optimal results. Instead, we present a novel approach calledlabel prompt dropout, which randomly removes label descriptions in the learningprocess. Our experiments show that our approach is able to lead to improvedclass representations, yielding significantly better results on the few-shotrelation extraction task.

Better Few-Shot Relation Extraction with Label Prompt Dropout