Selecting empathic response headers in customer support conversations with LLM-based emotion recognition

Abstract

This research considers the task of automatically adding empathic headers (e.g., “Sorry to hear that.”) to agent responses in customer support conversations. We employ a task-oriented dialogue (TOD) response selection model which allows response headers to be selected from existing corpora of conversations. Since the model is not fine-tuned with information about emotions in tweets, it is supplemented by filtering based on emotion annotations. The open-sourced LLM Llama 3.1 is employed for providing these annotations. We devise an experiment to evaluate this approach by automatic means. The preliminary results are discussed.