Daniel Douglass


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An Item Response Theory Framework for Persuasion
Anastassia Kornilova | Vladimir Eidelman | Daniel Douglass
Findings of the Association for Computational Linguistics: NAACL 2022

In this paper, we apply Item Response Theory, popular in education and political science research, to the analysis of argument persuasiveness in language. We empirically evaluate the model’s performance on three datasets, including a novel dataset in the area of political advocacy. We show the advantages of separating these components under several style and content representations, including evaluating the ability of the speaker embeddings generated by the model to parallel real-world observations about persuadability.