CS Colloquium: Hannaneh Hajishirzi (University of Washington)
Knowledge-Rich Neural Text Comprehension and Reasoning
Enormous amounts of ever-changing knowledge are available online in diverse textual styles (e.g., news vs. science text) and diverse formats (knowledge bases vs. web pages vs. textual documents). This talk presents the question of textual comprehension and reasoning given this diversity: how can AI help applications comprehend and combine evidence from variable, evolving sources of textual knowledge to make complex inferences and draw logical conclusions? I present question answering and fact checking algorithms that offer rich natural language comprehension using multi-hop and interpretable reasoning. Recent advances in deep learning algorithms, large-scale datasets, and industry-scale computational resources are spurring progress in many Natural Language Processing (NLP) tasks, including question answering. Nevertheless, current models lack the ability to answer complex questions that require them to reason intelligently across diverse sources and explain their decisions. Further, these models cannot scale up when task-annotated training data are scarce and computational resources are limited. With a focus on textual comprehension and reasoning, this talk will present some of the most recent efforts in my lab to integrate capabilities of symbolic AI approaches into current deep learning algorithms. I will present interpretable algorithms that understand and reason about textual knowledge across varied formats and styles, generalize to emerging domains with scarce training data (are robust), and operate efficiently under resource limitations (are scalable).
Bio: Hanna Hajishirzi is an Assistant Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington and a Research Fellow at the Allen Institute for AI. Her research spans different areas in NLP and AI, focusing on developing machine learning algorithms that represent, comprehend, and reason about diverse forms of data at large scale. Applications for these algorithms include question answering, reading comprehension, representation learning, knowledge extraction, and conversational dialogue. Honors include the Sloan Fellowship, Allen Distinguished Investigator Award, Intel rising star award, multiple 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.