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The Emotional Support Conversation (ESC) task aims to deliver consolation, encouragement, and advice to individuals undergoing emotional distress, thereby assisting them in overcoming difficulties. In the context of emotional support dialogue systems, it is of utmost importance to generate user-relevant and diverse responses. However, previous methods failed to take into account these crucial aspects, resulting in a tendency to produce universal and safe responses (e.g., “I do not know” and “I am sorry to hear that”). To tackle this challenge, a semantic-constrained bidirectional generation (SCBG) framework is utilized for generating more diverse and user-relevant responses. Specifically, we commence by selecting keywords that encapsulate the ongoing dialogue topics based on the context. Subsequently, a bidirectional generator generates responses incorporating these keywords. Two distinct methodologies, namely, statistics-based and prompt-based methods, are employed for keyword extraction. Experimental results on the ESConv dataset demonstrate that the proposed SCBG framework improves response diversity and user relevance while ensuring response quality.
Xu et al. (Mon,) studied this question.