通过关联可解释触发词与关系名称实现零样本对话关系抽取
Zero-Shot Dialogue Relation Extraction by Relating Explainable Triggers and Relation Names
AI总结：
针对对话关系抽取标注数据成本高、难扩展的问题，提出基于触发词捕捉关联未见过关系名称的零样本抽取模型，在DialogRE基准上已见和未见关系抽取均获显著提升，为提升DRE系统扩展性提供新方向。
摘要：
开发对话关系抽取（DRE）系统通常需要大量标注数据，其标注成本高昂且耗时。为提升可扩展性并支持对多样的、未见过的关系进行抽取，本文提出一种方法，利用捕捉触发词并将其与此前未见过的关系名称相关联的能力。具体而言，我们引入了一个模型，通过利用触发词捕捉能力实现零样本对话关系抽取。我们在基准数据集DialogRE上的实验表明，所提出的模型在已见和未见关系上均取得了显著提升。值得注意的是，这是首次尝试使用触发词捕捉能力进行零样本对话关系抽取，且我们的结果表明该方法对推断此前未见过的关系类型是有效的。总体而言，我们的发现凸显了该方法提升DRE系统可扩展性与实用性的潜力。
英文摘要：
Developing dialogue relation extraction (DRE) systems often requires a large amount of labeled data, which can be costly and time-consuming to annotate. In order to improve scalability and support diverse, unseen relation extraction, this paper proposes a method for leveraging the ability to capture triggers and relate them to previously unseen relation names. Specifically, we introduce a model that enables zero-shot dialogue relation extraction by utilizing trigger-capturing capabilities. Our experiments on a benchmark DialogRE dataset demonstrate that the proposed model achieves significant improvements for both seen and unseen relations. Notably, this is the first attempt at zero-shot dialogue relation extraction using trigger-capturing capabilities, and our results suggest that this approach is effective for inferring previously unseen relation types. Overall, our findings highlight the potential for this method to enhance the scalability and practicality of DRE systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
