基于提示与语义知识增强的零样本关系抽取
Prompt-based Zero-shot Relation Extraction with Semantic Knowledge Augmentation
AI总结：
针对零样本关系三元组抽取中未见关系识别难题，提出 ZS-SKA，通过类比增强实例、知识图谱加权虚拟标签提示和原型网络度量关系表示，在三个数据集上取得优于已有方法的效果。
摘要：
在关系三元组抽取（RTE）中，识别没有训练实例的未见关系是一项具有挑战性的任务。已有研究尝试基于问答模型或关系描述来识别未见关系。然而，这些方法忽略了已见关系与未见关系之间联系的语义信息。本文提出一种带有语义知识增强的基于提示的模型 ZS-SKA，用于在零样本设置下识别未见关系。作者提出一种新的词级类比句子转换规则，并利用该规则从已见关系实例生成带有未见关系的增强实例。同时，基于外部知识图谱设计了带加权虚拟标签构造的提示，以整合从已见关系中学到的语义知识信息。该方法不在提示模板中使用实际标签集合，而是构造加权虚拟标签词。模型借助增强实例和提示学习已见与未见关系的表示，随后使用原型网络计算所生成表示之间的距离，以预测未见关系。在 FewRel、Wiki-ZSL 和 NYT 三个公开数据集上进行的大量实验表明，ZS-SKA 在零样本设置下优于其他方法。结果还证明了 ZS-SKA 的有效性和鲁棒性。
英文摘要：
In relation triplet extraction (RTE), recognizing unseen relations for which there are no training instances is a challenging task. Efforts have been made to recognize unseen relations based on question-answering models or relation descriptions. However, these approaches miss the semantic information about connections between seen and unseen relations. In this paper, We propose a prompt-based model with semantic knowledge augmentation (ZS-SKA) to recognize unseen relations under the zero-shot setting. We present a new word-level analogy-based sentence translation rule and generate augmented instances with unseen relations from instances with seen relations using that new rule. We design prompts with weighted virtual label construction based on an external knowledge graph to integrate semantic knowledge information learned from seen relations. Instead of using the actual label sets in the prompt template, we construct weighted virtual label words. We learn the representations of both seen and unseen relations with augmented instances and prompts. We then calculate the distance between the generated representations using prototypical networks to predict unseen relations. Extensive experiments conducted on three public datasets FewRel, Wiki-ZSL, and NYT, show that ZS-SKA outperforms other methods under zero-shot setting. Results also demonstrate the effectiveness and robustness of ZS-SKA.
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