Emotion Cause Pair Extraction

Emotion-cause pair extraction (ECPE) aims to identify pairs of emotional expressions and their underlying causes within text, particularly in conversational contexts. Current research focuses on developing sophisticated models, often employing deep learning architectures like transformers, graph neural networks, and variational autoencoders, to overcome challenges such as data imbalance, spurious correlations, and the need for effective multi-task learning. These advancements are driven by the importance of understanding emotional dynamics in human-computer interaction, improving sentiment analysis, and enabling more nuanced applications in areas like mental health monitoring and customer service. The field is actively exploring methods to improve explainability and robustness, particularly in handling diverse modalities and conversational structures.