Better Few-Shot Relation Extraction with Label Prompt Dropout

TL;DR

This paper introduces label prompt dropout, a novel method that randomly omits label descriptions during training to improve class representations and enhance few-shot relation extraction performance.

Contribution

The paper proposes a new technique called label prompt dropout that improves few-shot relation extraction by better leveraging label information through random omission during training.

Findings

Significantly improved results on few-shot relation extraction tasks.

Effective in enhancing class representations.

Outperforms existing methods that use label information.

Abstract

Few-shot relation extraction aims to learn to identify the relation between two entities based on very limited training examples. Recent efforts found that textual labels (i.e., relation names and relation descriptions) could be extremely useful for learning class representations, which will benefit the few-shot learning task. However, what is the best way to leverage such label information in the learning process is an important research question. Existing works largely assume such textual labels are always present during both learning and prediction. In this work, we argue that such approaches may not always lead to optimal results. Instead, we present a novel approach called label prompt dropout, which randomly removes label descriptions in the learning process. Our experiments show that our approach is able to lead to improved class representations, yielding significantly better results on the few-shot relation extraction task.