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arXiv · 2203.12274

Pre-training to Match for Unified Low-shot Relation Extraction

Fangchao Liu·Hongyu Lin·Xianpei Han·Boxi Cao·Le Sun
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Abstract

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.

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Fangchao Liu, Hongyu Lin, Xianpei Han, Boxi Cao, Le Sun. 2022-03-23. Pre-training to Match for Unified Low-shot Relation Extraction. https://arxiv.org/abs/2203.12274

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