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Teaching computers to discover scientific knowledge by reading papers

Teaching computers to discover scientific knowledge by reading papers

Enormous amounts of ever-changing knowledge are available online in diverse emergent textual styles (e.g., news vs. science text). Recent advances in deep learning algorithms, large-scale datasets, and industry-scale computational resources are spurring progress in many Natural Language Processing (NLP)tasks. Nevertheless, current models lack the ability to understand emergent domains such as scientific articles related to Covid-19 when training data are scarce.
This talk presents some of recent efforts in our lab to address the problem of textual comprehension and reasoning about scientific articles. First, I discuss our multi-task learning approach for identifying and classifying entities and their relations in scientific articles. I further show that our approach supports construction of a scientific knowledge graph, which we use to analyze information in scientific literature. Second, I introduce scientific claim verification, a new task to select abstracts from the research literature containing evidence that supports or refutes a given scientific claim, and to identify rationales justifying each decision. I finally show that our claim verification system is able to identify plausible evidence for 70% claims relevant to COVID-19 on the CORD-19 corpus.

Keywords: Natural Language Processing, NLP for bio-medicine, Deep Learning, Knowledge extraction

About the speaker

Image Text 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, 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.

Organizers
R-Ladies East Lansing
Michigan Institute for Data Science
Camille Archer (RLEL, MSU), Janani Ravi (RLEL, MSU); Jing Liu (MIDAS, UM)


Program Committee
Liz Munch (MSU), Parisa Kordjamshidi (MSU), Dola Pathak (MSU)
Contact us: women [dot] plus [dot] datascience [at] gmail.com

Women+
Data Science

RLEL MIDAS MSU