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.
@article{arxiv.2203.12274,
title = {Pre-training to Match for Unified Low-shot Relation Extraction},
author = {Fangchao Liu and Hongyu Lin and Xianpei Han and Boxi Cao and Le Sun},
journal= {arXiv preprint arXiv:2203.12274},
year = {2022}
}