UC Santa Cruz

Depression and loneliness can be a serious social problem that affects people's physical and mental well-being. Our long-term goal is to develop a chatbot for well-being, WellBe, that can listen to people talk about their common daily events, acknowledge their feelings, and make responses to improve their well-being. We posit that developing WellBe requires: (1) the ability to classify user utterances to recognize the affective state of the user and the activity type that the user is talking about; and (2) effective dialogue strategies that condition on the classifier output and utilize theories of well-being. We assembled three corpora of user utterances representing both user affects and activities, and conducted a set of experiments within and across datasets. Our results show that a fine-tuned BERT model achieves F1 measures as high as .88 for user affect classification and .92 for user activity classification, and that it generalizes well across the different corpora. We then design rule-based response strategies that utilize these affect and activity classifiers, and conduct a human evaluation where we compare them with a baseline strategy that does not have access to the classifier output. The results show that responses that rely on the affect and activity classifiers are more engaging than the baseline, and that different response strategies are preferred depending on whether user affect is negative or positive.