In the Conversational AI and Social Analytics (CAISA) Lab, we combine diverse expertise from areas such as natural language processing, machine learning, and computational social sciences, on a mission to understand people behind the language.
We incorporate personal and social context into our language understanding and language generation models, aiming at building more user-centric conversational agents as well as more accurate web discourse interpretation systems.
Current areas of interest
User representation learning
How to represent users for social NLP tasks? Which subjective factors matter for interpreting a conversation? How to distinguish among users with varying preferences while preserving as much privacy as possible?
How to best model subjective human-machine dialogs, e.g. with opinionated agents? What are the user expectations on conversational AI in subjective areas, and how to evaluate these?
Opinion formation and dynamics
Which personal and social factors influence opinion changes and fluctuations? Which factors play a role in accepting and spreading misinformation, and can these be affected by the quality of a conversation?