Multi-dimensional Evaluation of Empathetic Dialog Responses
Zhichao Xu, Jiepu Jiang
Empathy is critical for effective and satisfactory conversational communication. Prior efforts to measure conversational empathy mostly focus on expressed communicative intents -- that is, the way empathy is expressed. Yet, these works ignore the fact that conversation is also a collaboration involving both speakers and listeners. In contrast, we propose a multi-dimensional empathy evaluation framework to measure both expressed intents from the speaker's perspective and perceived empathy from the listener's perspective. We apply our proposed framework to analyze our internal customer-service dialogue. We find the two dimensions (expressed intent types and perceived empathy) are inter-connected, and perceived empathy has a high correlation with dialogue satisfaction levels.   To reduce the annotation cost, we explore different options to automatically measure conversational empathy: prompting LLMs and training language model-based classifiers. Our experiments show that prompting methods with even popular models like GPT-4 and Flan family models perform relatively poorly on both public and our internal datasets. In contrast, instruction-finetuned classifiers based on Flan-T5 family models outperform prior works and competitive baselines. We conduct a detailed ablation study to give more insights into instruction finetuning method's strong performance.