论文标题
预训练以匹配统一的低射击关系提取
Pre-training to Match for Unified Low-shot Relation Extraction
论文作者
论文摘要
低射击关系提取〜（RE）旨在识别很少甚至没有样本的新型关系，这在实际场景应用中至关重要。很少有射击和零射击是两个代表性的低射击任务，这些任务似乎具有相似的目标，但需要完全不同的潜在能力。在本文中，我们提出了多选择匹配网络，以统一低射击关系提取。为了填补零射击和几次射击之间的空白，我们提出了三胞胎 - 跨度元训练，该元素利用三重态释义以预先培训零摄影标签的匹配能力，并使用元学习范式来学习少量射击实例实例实例的能力。在三个不同的低射击任务上的实验结果表明，该提出的方法的表现优于强大的基线，并在少数弹药排行榜上取得了最佳性能。
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.
