Observing Dialogue in Therapy: Categorizing and Forecasting Behavioral Codes
(a) Main results on forecasting client codes, in terms of F1 for St, Ct on dev set, and macro F1, and F1 for each client code on the test set.
['Method', 'Dev  Ct', 'Dev  St', 'Test macro', 'Test  Fn', 'Test  Ct', 'Test  St']
For client codes, we also report the Ct and St performance on the development set because of their importance. For the therapist codes, we also report the recall@3 to show the performance of a suggestion system that displayed three labels instead of one. The results show that even without an utterance, the dialogue history conveys signal about the next MISC label. Indeed, the performance for some labels is even better than some categorization baseline systems. For the therapist labels, if we always predicted the three most frequent labels (Fa, Gi, and Res), the recall@3 is only 67.7, suggesting that our models are informative if used in this suggestion-mode.