Low-shot relation extraction~(RE) aims to recognize novel relations with very
few or even no samples, which is critical in real scenario application.
Few-shot and zero-shot RE are two representative low-shot RE tasks, which seem
to be with similar target but require totally different underlying abilities.
In this paper, we propose Multi-Choice Matching Networks to unify low-shot
relation extraction. To fill in the gap between zero-shot and few-shot RE, we
propose the triplet-paraphrase meta-training, which leverages triplet
paraphrase to pre-train zero-shot label matching ability and uses meta-learning
paradigm to learn few-shot instance summarizing ability. Experimental results
on three different low-shot RE tasks show that the proposed method outperforms
strong baselines by a large margin, and achieve the best performance on
few-shot RE leaderboard.

本文提出了一种 Multi-Choice 匹配网络来统一少样本关系抽取，使用三元组改写元训练来填补零样本和少样本之间的差距，实验结果表明所提出的方法在三种不同的少样本关系抽取任务上表现优异，是少样本关系抽取领域中表现最好的模型。