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Developing dialogue relation extraction (DRE) systems often requires a largeamount of labeled data, which can be costly and time-consuming to annotate. Inorder to improve scalability and support diverse, unseen relation extraction,this paper proposes a method for leveraging the ability to capture triggers andrelate them to previously unseen relation names. Specifically, we introduce amodel that enables zero-shot dialogue relation extraction by utilizingtrigger-capturing capabilities. Our experiments on a benchmark DialogRE datasetdemonstrate that the proposed model achieves significant improvements for bothseen and unseen relations. Notably, this is the first attempt at zero-shotdialogue relation extraction using trigger-capturing capabilities, and ourresults suggest that this approach is effective for inferring previously unseenrelation types. Overall, our findings highlight the potential for this methodto enhance the scalability and practicality of DRE systems.

Zero-Shot Dialogue Relation Extraction by Relating Explainable Triggers and Relation Names