Abstract: Enormous amounts of ever-changing knowledge are available online in diverse textual styles and diverse formats. Recent advances in deep learning algorithms and large-scale datasets are spurring progress in many Natural Language Processing (NLP) tasks, including question answering. Nevertheless, these models cannot scale up when task-annotated training data are scarce. This talk presents my lab's work toward building general-purpose models in NLP and how to systematically evaluate them. I present a new meta-dataset – called super-Natural Instructions – that includes a variety of NLP tasks and their descriptions to evaluate cross-task generalization. Then, I introduce a new meta training approach that can solve more than 1600 NLP tasks only from their descriptions and a few examples.
Bio: Hanna Hajishirzi is an Associate Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington and a Senior Research Manager at the Allen Institute for AI. Her research spans different areas in NLP and AI, focusing on developing general-purpose machine learning algorithms that can solve diverse NLP tasks. Applications for these algorithms include question answering, representation learning, green AI, knowledge extraction, and conversational dialogue. Honors include the NSF CAREER Award, Sloan Fellowship, Allen Distinguished Investigator Award, Intel rising star award, best paper and honorable mention awards, and several industry research faculty awards. Hanna received her PhD from University of Illinois and spent a year as a postdoc at Disney Research and CMU.