Abstract

From education to medicine, our society relies on the success of everyday conversations. It is critical that we understand how this discourse occurs, what leads to success, and how it can be supported. In this talk, I discuss my work on goal-oriented conversations with a specific focus on empathetic and supportive interactions in clinical and counseling domains. I provide an overview of how natural language processing can be used to tackle problems in this domain, how it can provide insight into how our conversations work, and how it can be used to create tools to improve them.

Bio

Allison Lahnala is a Ph.D. Candidate at the University of Marburg, Germany. Her research focuses on computational social science and conversation dynamics in goal-oriented settings and interpersonal interactions involving particular social intents, such as persuasion and offering support. She is working to establish theory-driven empathy research approaches in Natural Language Processing (NLP) that consider the complex affective and cognitive processes and social factors that influence empathetic expression and perception. Currently, she is investigating cognitive-empathetic strategies in medical conversations for supporting patient-oriented treatment and therapeutic goals; particularly, she is working on “breaking bad news” clinical conversations. Her work with the Conversational AI and Social Analytics (CAISA) Lab also focuses on online forums, where they investigate stance dynamics and empathetic interactions, and transfer learning to better model social NLP tasks.