Zero-Shot Dialogue Relation Extraction by Relating Explainable Triggers
  and Relation Names
Ze-Song Xu Yun-Nung Chen
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.