A rhetorical structure theory and inference-aware graph network for emotion-cause pair extraction

Emotion-Cause Pair Extraction (ECPE) aims to identify and extract emotion clauses and their corresponding cause clauses from unannotated emotional texts. Previous approaches primarily leveraged contextual features or attention weights as semantic information to enhance feature representations. However, these methods often overlook the additional causal information that external knowledge can bring to ECPE. Given the evident inferential relationship between emotions and their causes, we propose a novel model called Rhetorical Structure Theory and Inference-Aware Graph networks (RSTIAG) to address the challenges posed by ECPE. This model constructs a semantic dependency space grounded in discourse-level rhetorical structure theory and a symbolic reasoning space based on an inference-aware graph. By integrating the introduced knowledge through a space fusion co-attention mechanism and modeling the dependencies between these two spaces, the model enables clauses to simultaneously capture diverse knowledge from both spaces. Additionally, we enrich the information dimensions of emotion-cause pairs by instantiating the reasoning process from the perspectives of causality and implicit inference. To substantiate the effectiveness of our proposed RSTIAG model, we undertake comprehensive experiments. The results demonstrate that it significantly outperforms 11 baseline models on an English benchmark corpus, confirming its effectiveness.