Computational modeling of conversational humor in psychotherapy

Humor is an important social construct that serves several roles in human communication. Though subjective, it is culturally ubiquitous and is often used to diffuse tension, specially in intense conversations such as those in psychotherapy sessions. Automatic recognition of humor has been of considerable interest in the natural language processing community thanks to its relevance in conversational agents. In this work, we present a model for humor recognition in Motivational Interviewing based psychotherapy sessions. We use a Long Short Term Memory (LSTM) based recurrent neural network sequence model trained on dyadic conversations from psychotherapy sessions and our model outperforms a standard baseline with linguistic humor features.