论文标题
PCRED：零射击关系三重提取，潜在的候选关系选择和实体边界检测
PCRED: Zero-shot Relation Triplet Extraction with Potential Candidate Relation Selection and Entity Boundary Detection
论文作者
论文摘要
零射线关系三重态提取（Zerorte）旨在从零射击设置下的非结构化文本中提取关系三重态，其中训练和测试阶段的关系集是不相交的。以前的最先进方法通过利用预验证的语言模型作为额外的培训样本来处理这项具有挑战性的任务，从而增加了培训成本并严重限制了模型性能。为了解决上述问题，我们提出了一种名为Zerorte的新方法，该方法具有潜在的候选关系选择和实体边界检测。 PCRED的显着特征是，它不依赖其他数据，并且仍然可以实现有希望的性能。该模型采用关系优先的范式，通过候选关系选择来认识到看不见的关系。通过这种方法，关系的语义自然是在上下文中注入的。根据随后关系的上下文和语义提取实体。我们在两个Zerorte数据集上评估了我们的模型。实验结果表明，我们的方法始终优于先前的工作。我们的代码将在https://anonymon.4open.science/r/pcred上找到。
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.
