Knowledge mismatch is inherent to communicative interaction, since conversational partners naturally differ in their knowledge and beliefs.  Effective conversational moves, such as asking a question or referring to an entity, crucially depend then on each participant acting appropriately with respect to which knowledge is privileged (unique to that participant) and which is shared among the participants.  For example, much work in linguistics suggests that a definite referring expression such as the knife must be interpreted in the context of mutual knowledge (“common ground”) in which there is a uniquely identifiable knife relevant to both conversational partners.

What has been less clear is the moment-by-moment cognitive processing by which people reconcile knowledge mismatch in language production and interpretation.  We propose a theory and computational model in which differing views of relevant knowledge, both privileged to the individual and shared with the conversational partner, are simultaneously and probabilistically considered at the earliest moments of language processing.  This approach arises from a view of communicative interaction as a process requiring continual and rapid accessing and updating of both privileged and shared knowledge.  Moreover, the probabilistic formulation emphasizes the uncertainty inherent in assessing the knowledge and beliefs of a conversational partner, and suggests that accommodating such uncertainty is therefore an inherent aspect of online language processing.

This is joint work with Daphna Heller, Associate Professor of Linguistics, University of Toronto, and Mindaugas Mozuraitis, Team Lead, Cancer Analytics at Cancer Care Ontario.