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EMNLP 2024mainmain

AutoPersuade: A Framework for Evaluating and Explaining Persuasive Arguments

Till Raphael Saenger, Musashi Hinck, Justin Grimmer, Brandon M. Stewart

Intel · Stanford University

PDF 由论文原始站点提供,PaperCompass 不保存论文文件。DOI 10.18653/v1/2024.emnlp-main.913 ↗

摘要

We introduce a three-part framework for constructing persuasive messages, AutoPersuade. First, we curate a large collection of arguments and gather human evaluations of their persuasiveness. Next, we introduce a novel topic model to identify the features of these arguments that influence persuasion. Finally, we use the model to predict the persuasiveness of new arguments and to assess the causal effects of argument components, offering an explanation of the results. We demonstrate the effectiveness of AutoPersuade in an experimental study on arguments for veganism, validating our findings through human studies and out-of-sample predictions.