Dialogue-based Relation Extraction (DRE) aims to predict the relation type of argument pairs that are mentioned in dialogue. The latest trigger-enhanced methods propose trigger prediction tasks to promote DRE. However, these methods are not able to fully leverage the trigger information and even bring noise to relation extraction. To solve these problems, we propose TLAG, which fully leverages the trigger and label-aware knowledge to guide the relation extraction. First, we design an adaptive trigger fusion module to fully leverage the trigger information. Then, we introduce label-aware knowledge to further promote our model's performance. Experimental results on the DialogRE dataset show that our TLAG outperforms the baseline models, and detailed analyses demonstrate the effectiveness of our approach.

翻译：对话关系抽取（DRE）旨在预测对话中提及的论元对之间的关系类型。最新的触发器增强方法提出了触发器预测任务以促进DRE。然而，这些方法未能充分利用触发器信息，甚至可能给关系抽取带来噪声。为解决这些问题，我们提出了TLAG，该模型充分利用触发器和标签感知知识来指导关系抽取。首先，我们设计了一个自适应触发器融合模块，以充分利用触发器信息。然后，我们引入标签感知知识以进一步提升模型性能。在DialogRE数据集上的实验结果表明，我们的TLAG优于基线模型，详细分析证明了我们方法的有效性。