Modeling Empathetic Alignment in Conversation
This work addresses the challenge of modeling empathy in NLP for applications like mental health support, though it is incremental as it builds on existing theories and datasets.
The paper tackled the problem of recognizing empathetic alignment in conversation by introducing a new dataset with span-level annotations and computational experiments, showing that appraisals and alignments can be accurately recognized, with mental health professionals engaging in substantially more empathetic alignment.
Empathy requires perspective-taking: empathetic responses require a person to reason about what another has experienced and communicate that understanding in language. However, most NLP approaches to empathy do not explicitly model this alignment process. Here, we introduce a new approach to recognizing alignment in empathetic speech, grounded in Appraisal Theory. We introduce a new dataset of over 9.2K span-level annotations of different types of appraisals of a person's experience and over 3K empathetic alignments between a speaker's and observer's speech. Through computational experiments, we show that these appraisals and alignments can be accurately recognized. In experiments in over 9.2M Reddit conversations, we find that appraisals capture meaningful groupings of behavior but that most responses have minimal alignment. However, we find that mental health professionals engage with substantially more empathetic alignment.