Zero-shot relation triplet extraction (ZeroRTE) aims to extract relation
triplets from unstructured texts under the zero-shot setting, where the
relation sets at the training and testing stages are disjoint. Previous
state-of-the-art method handles this challenging task by leveraging pretrained
language models to generate data as additional training samples, which
increases the training cost and severely constrains the model performance. To
address the above issues, we propose a novel method named PCRED for ZeroRTE
with Potential Candidate Relation Selection and Entity Boundary Detection. The
remarkable characteristic of PCRED is that it does not rely on additional data
and still achieves promising performance. The model adopts a relation-first
paradigm, recognizing unseen relations through candidate relation selection.
With this approach, the semantics of relations are naturally infused in the
context. Entities are extracted based on the context and the semantics of
relations subsequently. We evaluate our model on two ZeroRTE datasets. The
experiment results show that our method consistently outperforms previous
works. Our code will be available at https://anonymous.4open.science/r/PCRED.

本研究介绍了一种名为 PCRED 的零样本关系三元组抽取方法，采用候选关系选择和实体边界检测的方法，不依赖于额外的数据且表现优异，最终实验结果表明 PCRED 方法始终优于以往研究。