  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.
