Hanna Hajishirzi (University of Washington & Allen Institute for AI ) “Toward Robust, Knowledge-Rich NLP”

February 25, 2022 @ 12:00 pm – 1:15 pm
Ames Hall 234 - Presented Virtually Via Zoom https://wse.zoom.us/j/96735183473


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. First, I present a general model for two known tasks of question answering in English and multiple languages that are robust to small domain shifts.  Then, I show a meta-training approach that can solve a variety of NLP tasks with only using a few examples and introduce a benchmark to evaluate cross-task generalization. Finally, I discuss neuro-symbolic approaches to address more complex tasks by eliciting knowledge from structured data and language models.
Hanna Hajishirzi is an Assistant 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 many 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.

Center for Language and Speech Processing