Multi-dimensional Evaluation of Empathetic Dialog Responses

• URL: http://arxiv.org/abs/2402.11409v3
• Date: Fri, 11 Oct 2024 22:30:05 GMT
• Title: Multi-dimensional Evaluation of Empathetic Dialog Responses
• Authors: Zhichao Xu, Jiepu Jiang,
• Abstract summary: We propose a multi-dimensional empathy evaluation framework to measure both emphexpressed intents from the speaker's perspective and emphperceived empathy from the listener's perspective. We find the two dimensions are inter-connected, while perceived empathy has high correlations with dialogue satisfaction levels.
• Score: 4.580983642743026
• License:
• Abstract: 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 \emph{expressed intents from the speaker's perspective} and \emph{perceived empathy from the listener's perspective}. We apply our analytical framework to examine internal customer-service dialogues. We find the two dimensions (expressed intent types and perceived empathy) are inter-connected, while perceived empathy has high correlations 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.

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