Workshop on Insights from Negative Results in NLP
Budapest, Hungary, October 22-29, 2026
(co-located with EMNLP. Exact day TBA)
Invited Speakers
Ryan L. Boyd
What Is a Result a Result About?
Abstract. Empirical findings are rarely about the single thing that we take them to be about. Any given result is the product of several intersecting factors: a hypothesis, a measure, an operationalization, and a considerable number of assumptions that are unexamined. When the bundle performs well, we credit the hypothesis; when it performs poorly, we fault the hypothesis as well. Neither inference is especially well-founded. In this talk, I draw on case material and several long-standing arguments from the philosophy of science to consider what our results are actually about, and what it would take to find out.
Bio. Ryan L. Boyd is an Assistant Professor of Connected Computing at Vanderbilt University. His research applies natural language processing and machine learning to the study of verbal behavior, developing computational systems for measuring and understanding psychological constructs — including mental health, self-perception, extremism, and social influence — in large-scale naturalistic data. A recurring concern in his research is the gap between what language-based models are assumed to measure and what they actually capture, and the methodological consequences of that gap for using text as psychological measurement. He has published over 100 scholarly works across CS venues (ACL, NAACL, ICWSM) and psychology journals (American Psychologist, Perspectives on Psychological Science, Nature Human Behaviour), and holds a PhD in Social and Personality Psychology from the University of Texas at Austin.
Dirk Hovy
Talk details to be announced.
Anya Belz
Talk details to be announced.