Applying Emotion-Cause Entailment for Help-Seeker Guidance in Emotional Support Conversations

Developing a dialogue system for emotional support conversations (ESCs) is challenging, as it requires addressing both general dialogue aspects and dynamic emotional interactions with help-seekers. Existing systems often fail to adapt to help-seekers' emotional changes, which are crucial for effective support. A supporter must understand when to initiate guidance; otherwise, help-seekers may reject support due to negative emotions. This study introduces the dynamic plug-and-play language model (DPPLM), which integrates a transformer-based language model with two attribute models to generate strategic and empathetic responses. Unlike the original plug-and-play framework, DPPLM tracks help-seekers' emotional states using emotion-cause entailment, enabling better timing and balance between strategy and empathy. Evaluated on the emotional support conversation (ESConv) dataset, DPPLM consistently delivered stage-appropriate responses, providing greater comfort and support. It achieved a BERTScore of 0.8451, a ROUGE-L of 12.10, a Distinct-1 of 4.63, a Distinct-2 of 32.24, an emotion accuracy of 65.42%, and a strategy accuracy of 58.03%. In human evaluations, DPPLM outperformed baseline systems in overall performance, demonstrating its effectiveness in ESCs.