Jingwen Zhang


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Social Influence Dialogue Systems: A Survey of Datasets and Models For Social Influence Tasks
Kushal Chawla | Weiyan Shi | Jingwen Zhang | Gale Lucas | Zhou Yu | Jonathan Gratch
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

Dialogue systems capable of social influence such as persuasion, negotiation, and therapy, are essential for extending the use of technology to numerous realistic scenarios. However, existing research primarily focuses on either task-oriented or open-domain scenarios, a categorization that has been inadequate for capturing influence skills systematically. There exists no formal definition or category for dialogue systems with these skills and data-driven efforts in this direction are highly limited. In this work, we formally define and introduce the category of social influence dialogue systems that influence users’ cognitive and emotional responses, leading to changes in thoughts, opinions, and behaviors through natural conversations. We present a survey of various tasks, datasets, and methods, compiling the progress across seven diverse domains. We discuss the commonalities and differences between the examined systems, identify limitations, and recommend future directions. This study serves as a comprehensive reference for social influence dialogue systems to inspire more dedicated research and discussion in this emerging area.


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Seamlessly Integrating Factual Information and Social Content with Persuasive Dialogue
Maximillian Chen | Weiyan Shi | Feifan Yan | Ryan Hou | Jingwen Zhang | Saurav Sahay | Zhou Yu
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Complex conversation settings such as persuasion involve communicating changes in attitude or behavior, so users’ perspectives need to be addressed, even when not directly related to the topic. In this work, we contribute a novel modular dialogue system framework that seamlessly integrates factual information and social content into persuasive dialogue. Our framework is generalizable to any dialogue tasks that have mixed social and task contents. We conducted a study that compared user evaluations of our framework versus a baseline end-to-end generation model. We found our model was evaluated to be more favorable in all dimensions including competence and friendliness compared to the baseline model which does not explicitly handle social content or factual questions.


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Evaluation of In-Person Counseling Strategies To Develop Physical Activity Chatbot for Women
Kai-Hui Liang | Patrick Lange | Yoo Jung Oh | Jingwen Zhang | Yoshimi Fukuoka | Zhou Yu
Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue

Artificial intelligence chatbots are the vanguard in technology-based intervention to change people’s behavior. To develop intervention chatbots, the first step is to understand natural language conversation strategies in human conversation. This work introduces an intervention conversation dataset collected from a real-world physical activity intervention program for women. We designed comprehensive annotation schemes in four dimensions (domain, strategy, social exchange, and task-focused exchange) and annotated a subset of dialogs. We built a strategy classifier with context information to detect strategies from both trainers and participants based on the annotation. To understand how human intervention induces effective behavior changes, we analyzed the relationships between the intervention strategies and the participants’ changes in the barrier and social support for physical activity. We also analyzed how participant’s baseline weight correlates to the amount of occurrence of the corresponding strategy. This work lays the foundation for developing a personalized physical activity intervention chatbot.


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Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good
Xuewei Wang | Weiyan Shi | Richard Kim | Yoojung Oh | Sijia Yang | Jingwen Zhang | Zhou Yu
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Developing intelligent persuasive conversational agents to change people’s opinions and actions for social good is the frontier in advancing the ethical development of automated dialogue systems. To do so, the first step is to understand the intricate organization of strategic disclosures and appeals employed in human persuasion conversations. We designed an online persuasion task where one participant was asked to persuade the other to donate to a specific charity. We collected a large dataset with 1,017 dialogues and annotated emerging persuasion strategies from a subset. Based on the annotation, we built a baseline classifier with context information and sentence-level features to predict the 10 persuasion strategies used in the corpus. Furthermore, to develop an understanding of personalized persuasion processes, we analyzed the relationships between individuals’ demographic and psychological backgrounds including personality, morality, value systems, and their willingness for donation. Then, we analyzed which types of persuasion strategies led to a greater amount of donation depending on the individuals’ personal backgrounds. This work lays the ground for developing a personalized persuasive dialogue system.