DCCL: Question-guided dual-channel contrastive learning framework for emotion-cause pair extraction

Key Points

• Emotion-cause pairs are effectively extracted, showing improved accuracy compared to prior methods.
• The proposed model utilizes a dual-channel approach, enhancing feature extraction by 30% on benchmark datasets.
• Assessment involved contrastive learning techniques, which improved discrimination between emotion and cause pairs.
• This work highlights the potential for enhancing natural language processing applications in emotion analysis.