CLApr 24, 2022

An Item Response Theory Framework for Persuasion

arXiv:2204.11337v1628 citationsh-index: 12
Originality Synthesis-oriented
AI Analysis

This addresses the need for better persuasion analysis in language, particularly for political advocacy, but is incremental as it adapts an existing framework to a new domain.

The paper tackled the problem of analyzing argument persuasiveness in language by applying Item Response Theory, showing advantages in separating components under various style and content representations and evaluating speaker embeddings against real-world persuadability observations.

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.

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