AdMISC: Advanced Multi-Task Learning and Feature-Fusion for Emotional Support Conversation

Abstract

The emotional support dialogue system is an emerging and challenging task in natural language processing to alleviate people’s emotional distress. Each utterance in the dialogue has features such as emotion, intent, and commonsense knowledge. Previous research has indicated subpar performance in strategy prediction accuracy and response generation quality due to overlooking certain underlying factors. To address these issues, we propose Advanced Multi-Task Learning and Feature-Fusion for Emotional Support Conversation (AdMISC), which extracts various potential factors influencing dialogue through neural networks, thereby improving the accuracy of strategy prediction and the quality of generated responses. Specifically, we extract features affecting dialogue through dynamic emotion extraction and commonsense enhancement and then model strategy prediction. Additionally, the model learns these features through attention networks to generate higher quality responses. Furthermore, we introduce a method for automatically averaging loss function weights to improve the model’s performance. Experimental results using the emotional support conversation dataset ESConv demonstrate that our proposed model outperforms baseline methods in both strategy label prediction accuracy and a range of automatic and human evaluation metrics.