Event Details

Despite remarkable progress in building Question Answering (QA) models, the scope of progress remains limited to niche dataset-specific domains. How can we expand the scope of the problems that our models can address? In this talk, I discuss two instances of QA system design that cover a broader range of problems. In the first part, I introduce UnifiedQA, a single model that generalizes to multiple different QA formats (multiple-choice QA, extractive QA, abstractive QA, yes-no QA). Then I will introduce ModularQA, a single system that addresses multiple multi-hop reasoning datasets by leveraging existing single-hop modules (systems). For each system, I present empirical evidence on their better generalization and stronger robustness across datasets and domains.

Speaker Bio

Daniel Khashabi is a “Young Investigator” at Allen Institute for AI, Seattle. His interests lie at the intersection of artificial intelligence and natural language processing. He earned his Ph.D. from the University of Pennsylvania and his undergraduate degree from Amirkabir University of Technology (Tehran Polytechnic).