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

• URL: http://arxiv.org/abs/2203.12274v1
• Date: Wed, 23 Mar 2022 08:43:52 GMT
• Title: Pre-training to Match for Unified Low-shot Relation Extraction
• Authors: Fangchao Liu, Hongyu Lin, Xianpei Han, Boxi Cao, Le Sun
• Abstract summary: Low-shot relation extraction aims to recognize novel relations with very few or even no samples. Few-shot and zero-shot RE are two representative low-shot RE tasks. We propose Multi-Choice Matching Networks to unify low-shot relation extraction.
• Score: 37.625078897220305
• License: http://creativecommons.org/licenses/by/4.0/
• 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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