Guest lecture – Vered Shwartz – Incorporating Commonsense Reasoning into NLP Models

Location:

Engineering building (1103), room 329

Title:

Incorporating Commonsense Reasoning into NLP Models

Abstract:

Human language is often ambiguous, underspecified, and grounded in social norms. We employ commonsense knowledge and reasoning abilities to understand others. Endowing NLP models with the same abilities is imperative for reaching human-level language understanding and generation skills. In this talk, I will present several lines of work in which commonsense is used for improving the performance of NLP tasks: for resolving context-sensitive event coreferences, interpreting rare and ambiguous phrases, and expanding underspecified contexts in QA. Finally, I will discuss open problems and future directions in building NLP models with commonsense reasoning abilities.

Short Bio:

Vered Shwartz is an Assistant Professor of Computer Science at the University of British Columbia and a CIFAR AI Chair at the Vector Institute for Artificial Intelligence. Her research interests include commonsense reasoning, computational semantics and pragmatics, and multiword expressions. Previously, Vered was a postdoctoral researcher at the Allen Institute for AI (AI2) and the University of Washington, and received her PhD in Computer Science from Bar-Ilan University.