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        <title>Understanding User Resistance Strategies in Persuasive Conversations</title>
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        <namePart type="given">Youzhi</namePart>
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    <name type="personal">
        <namePart type="given">Zhou</namePart>
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    <abstract>Persuasive dialog systems have various usages, such as donation persuasion and physical exercise persuasion. Previous persuasive dialog systems research mostly focused on analyzing the persuader’s strategies and paid little attention to the persuadee (user). However, understanding and addressing users’ resistance strategies is an essential job of a persuasive dialog system. So, we adopt a preliminary framework on persuasion resistance in psychology and design a fine-grained resistance strategy annotation scheme. We annotate the PersuasionForGood dataset with the scheme. With the enriched annotations, we build a classifier to predict the resistance strategies. Furthermore, we analyze the relationships between persuasion strategies and persuasion resistance strategies. Our work lays the ground for developing a persuasive dialogue system that can understand and address user resistance strategy appropriately. The code and data will be released.</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.431</identifier>
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%0 Conference Proceedings
%T Understanding User Resistance Strategies in Persuasive Conversations
%A Tian, Youzhi
%A Shi, Weiyan
%A Li, Chen
%A Yu, Zhou
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F tian-etal-2020-understanding
%X Persuasive dialog systems have various usages, such as donation persuasion and physical exercise persuasion. Previous persuasive dialog systems research mostly focused on analyzing the persuader’s strategies and paid little attention to the persuadee (user). However, understanding and addressing users’ resistance strategies is an essential job of a persuasive dialog system. So, we adopt a preliminary framework on persuasion resistance in psychology and design a fine-grained resistance strategy annotation scheme. We annotate the PersuasionForGood dataset with the scheme. With the enriched annotations, we build a classifier to predict the resistance strategies. Furthermore, we analyze the relationships between persuasion strategies and persuasion resistance strategies. Our work lays the ground for developing a persuasive dialogue system that can understand and address user resistance strategy appropriately. The code and data will be released.
%R 10.18653/v1/2020.findings-emnlp.431
%U https://aclanthology.org/2020.findings-emnlp.431
%U https://doi.org/10.18653/v1/2020.findings-emnlp.431
%P 4794-4798

Markdown (Informal)

[Understanding User Resistance Strategies in Persuasive Conversations](https://aclanthology.org/2020.findings-emnlp.431) (Tian et al., Findings 2020)

• Understanding User Resistance Strategies in Persuasive Conversations (Tian et al., Findings 2020)

ACL

• Youzhi Tian, Weiyan Shi, Chen Li, and Zhou Yu. 2020. Understanding User Resistance Strategies in Persuasive Conversations. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4794–4798, Online. Association for Computational Linguistics.