Muffin: Mitigating Unhelpfulness in Emotional Support Conversations with Multifaceted AI Feedback

Anonymous

Abstract: Emotional support conversation systems are designed to alleviate users' emotional distress and assist them in overcoming their challenges. While previous studies have made progress, their models occasionally generate unhelpful responses, which are intended to be supportive but instead have counterproductive effects. Since unhelpful responses can hinder the effectiveness of emotional support, it is crucial to mitigate them within conversations. Our solution is motivated by two principal considerations: (1) multiple facets of emotional support are expected to be considered when developing emotional support conversation models, and (2) directly reducing the probability of generating unhelpful responses can effectively mitigate their occurrence. Accordingly, we introduce a novel $\textbf{model-agnostic}$ framework named $\underline{M}$itigating $\underline{u}$nhelpfulness with multi\underline{f}aceted AI $\underline{f}$eedback for emot$\underline{i}$o$\underline{n}$al support ($\textit{Muffin}$). It first employs a multifaceted AI feedback module designed to assess the helpfulness model responses across various facets of emotional support. Leveraging contrastive learning, Muffin then reduces the unhelpful responses' likelihoods. To validate the effectiveness of our proposed framework, we apply Muffin to various previous emotional support generation models, including the state-of-the-art. Experimental results demonstrate that Muffin can significantly mitigate unhelpful response generation while enhancing response fluency and relevance.

Paper Type: long

Research Area: Dialogue and Interactive Systems

Contribution Types: NLP engineering experiment

Languages Studied: English