PhD defence by Dustin Brandon Wright

Title

Machine Understanding of Scientific Language

Abstract

Scientific information expresses human understanding of nature. This knowledge is largely disseminated in different forms of text, including scientific papers, news articles, and discourse among people on social media. While important for accelerating our
pursuit of knowledge, not all scientific text is faithful to the underlying science. As the volume of this text has burgeoned online in recent years, it has become a problem of societal importance to be able to identify the faithfulness of a given piece of scientific text automatically. This thesis is concerned with the cultivation of datasets, methods, and tools for machine understanding of scientific language, in order to analyze and understand science communication at scale. To arrive at this, I present several contributions in three areas of natural language processing and machine learning:
automatic fact checking, learning with limited data, and scientific text processing. These contributions include new methods and resources for identifying check-worthy claims, adversarial claim generation, multi-source domain adaptation, learning from crowdsourced labels, cite-worthiness detection, zero-shot scientific fact checking, detecting exaggerated scientific claims, and modeling degrees of information change in science communication. Critically, I demonstrate how the research outputs of this thesis are useful for effectively learning from limited amounts of scientific text in order to identify misinformative scientific statements and generate new insights into the science communication process.

Supervisor

Principal Supervisor: Professor Isabelle Augenstein

Assessment Committee

Professor Serge Belongie (chair Peron), Computer Science, Copenhagen University
Associate Professor Andreas Vlachos, University of Cambridge, UK
Research Scientist Smaranda Muresam, Columbia University, USA

Leader of defense: Daniel Hershcovich

For an electronic copy of the thesis, please visit the PhD Programme page.