Matthew Wilkens — accepted AI-conference papers

2025

Disentangling language change: sparse autoencoders quantify the semantic evolution of indigeneity in French

Jacob A. Matthews, Laurent Dubreuil, Imane Terhmina, Yunci Sun, Matthew Wilkens, Marten Van Schijndel

NAACL 2025long

This study presents a novel approach to analyzing historical language change, focusing on the evolving semantics of the French term “indigène(s)” (“indigenous”) between 1825 and 1950. While existing approaches to measuring semantic change with contextual word embeddings (CWE) rely primarily on simil…

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2023

Grounding Characters and Places in Narrative Text

Sandeep Soni, Amanpreet Sihra, Elizabeth Evans, Matthew Wilkens, David Bamman

ACL 2023long

Tracking characters and locations throughout a story can help improve the understanding of its plot structure. Prior research has analyzed characters and locations from text independently without grounding characters to their locations in narrative time. Here, we address this gap by proposing a new…

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2023

Modeling Legal Reasoning: LM Annotation at the Edge of Human Agreement

Rosamond Elizabeth Thalken, Edward Stiglitz, David Mimno, Matthew Wilkens

EMNLP 2023long main

Generative language models (LMs) are increasingly used for document class-prediction tasks and promise enormous improvements in cost and efficiency. Existing research often examines simple classification tasks, but the capability of LMs to classify on complex or specialized tasks is less well unders…

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