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Dialogue-based Relation Extraction (DRE) aims to predict the relation type ofargument pairs that are mentioned in dialogue. The latest trigger-enhancedmethods propose trigger prediction tasks to promote DRE. However, these methodsare not able to fully leverage the trigger information and even bring noise torelation extraction. To solve these problems, we propose TLAG, which fullyleverages the trigger and label-aware knowledge to guide the relationextraction. First, we design an adaptive trigger fusion module to fullyleverage the trigger information. Then, we introduce label-aware knowledge tofurther promote our model's performance. Experimental results on the DialogREdataset show that our TLAG outperforms the baseline models, and detailedanalyses demonstrate the effectiveness of our approach.

TLAG: An Informative Trigger and Label-Aware Knowledge Guided Model for Dialogue-based Relation Extraction