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View all- Puspitasari FTyson GHaq EHui PLee L(2023)Ghost Booking as a New Philanthropy ChannelProceedings of the 34th ACM Conference on Hypertext and Social Media10.1145/3603163.3609028(1-11)Online publication date: 4-Sep-2023
How to Ask for Donations? Learning User-Specific Persuasive Dialogue Policies through Online Interactions
Pages 12 - 22
Abstract
Persuasive conversations are more effective when they are custom-tailored for the intended audience. Current persuasive dialogue systems rely heavily on advice-giving or focus on different framing policies in a constrained and less dynamic/flexible manner. In this paper, we argue for a new approach, in which the system can identify optimal persuasive strategies in context and persuade users through online interactions. We study two main questions (1) can a reinforcement-learning-based dialogue framework learn to exercise user-specific communicative strategies for persuading users? (2) How can we leverage the crowd-sourcing platforms to collect data for training, and evaluating such frameworks for human-AI(/machine) conversations? We describe a prototype system that interacts with users with the goal of persuading them to donate to a charity and use experiments with crowd workers and analyses of our learned policies to document that our approach leads to learning context-sensitive persuasive strategies that focus on user’s reactions towards donation and contribute to increasing dialogue success.
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References
[1]
Ifeoma Adaji, Kiron Nafisul, and Julita Vassileva. 2021. Level of Involvement and the Influence of Persuasive Strategies in E-Commerce: A Game-Based Approach. Association for Computing Machinery, New York, NY, USA, 325–332. https://doi.org/10.1145/3450614.3464626
[2]
Alaa Alslaity and Thomas Tran. 2021. Goal Modeling-Based Evaluation for Personalized Recommendation Systems. Association for Computing Machinery, New York, NY, USA, 276–283. https://doi.org/10.1145/3450614.3464619
[3]
Sara Asai, Koichiro Yoshino, Seitaro Shinagawa, Sakriani Sakti, and Satoshi Nakamura. 2020. Emotional Speech Corpus for Persuasive Dialogue System. In Proceedings of the 12th Language Resources and Evaluation Conference. European Language Resources Association, Marseille, France, 491–497. https://aclanthology.org/2020.lrec-1.62
[4]
Tuhin Chakrabarty, Christopher Hidey, Smaranda Muresan, Kathy McKeown, and Alyssa Hwang. 2019. AMPERSAND: Argument Mining for PERSuAsive oNline Discussions. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, 2933–2943. https://doi.org/10.18653/v1/D19-1291
[5]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, Minneapolis, Minnesota, 4171–4186. https://doi.org/10.18653/v1/N19-1423
[6]
He He, Derek Chen, Anusha Balakrishnan, and Percy Liang. 2018. Decoupling Strategy and Generation in Negotiation Dialogues. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Brussels, Belgium, 2333–2343. https://doi.org/10.18653/v1/D18-1256
[7]
Christopher Hidey, Elena Musi, Alyssa Hwang, Smaranda Muresan, and Kathy McKeown. 2017. Analyzing the Semantic Types of Claims and Premises in an Online Persuasive Forum. In Proceedings of the 4th Workshop on Argument Mining. Association for Computational Linguistics, Copenhagen, Denmark, 11–21. https://doi.org/10.18653/v1/W17-5102
[8]
Takuya Hiraoka, Graham Neubig, Sakriani Sakti, Tomoki Toda, and Satoshi Nakamura. 2014. Reinforcement Learning of Cooperative Persuasive Dialogue Policies using Framing. In Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers. Dublin City University and Association for Computational Linguistics, Dublin, Ireland, 1706–1717. https://aclanthology.org/C14-1161
[9]
C. Hutto and Eric Gilbert. 2014. VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text. Proceedings of the International AAAI Conference on Web and Social Media 8, 1 (May 2014), 216–225. https://ojs.aaai.org/index.php/ICWSM/article/view/14550
[10]
Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1412.6980
[11]
Mike Lewis, Denis Yarats, Yann Dauphin, Devi Parikh, and Dhruv Batra. 2017. Deal or No Deal? End-to-End Learning of Negotiation Dialogues. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Copenhagen, Denmark, 2443–2453. https://doi.org/10.18653/v1/D17-1259
[12]
Yu Li, Kun Qian, Weiyan Shi, and Zhou Yu. 2020. End-to-end trainable non-collaborative dialog system. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 8293–8302.
[13]
Stephanie Lukin, Pranav Anand, Marilyn Walker, and Steve Whittaker. 2017. Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers. Association for Computational Linguistics, Valencia, Spain, 742–753. https://aclanthology.org/E17-1070
[14]
Alexander Miller, Will Feng, Dhruv Batra, Antoine Bordes, Adam Fisch, Jiasen Lu, Devi Parikh, and Jason Weston. 2017. ParlAI: A Dialog Research Software Platform. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Association for Computational Linguistics, Copenhagen, Denmark, 79–84. https://doi.org/10.18653/v1/D17-2014
[15]
Dinesh Mulchandani and Rita Orji. 2021. Persuasiveness of a Game to Promote the Adoption of COVID-19 Precautionary Measures and the Moderating Effect of Gender. Association for Computing Machinery, New York, NY, USA, 303–308. https://doi.org/10.1145/3450614.3464623
[16]
Makuochi Nkwo, Rita Orji, and Ifeyinwa Ajah. 2021. A Health Belief Model Approach to Evaluating Maternal Health Behaviors among Africans - Design Implications for Personalized Persuasive Technologies. Association for Computing Machinery, New York, NY, USA, 309–317. https://doi.org/10.1145/3450614.3464624
[17]
Fidelia A. Orji, Kiemute Oyibo, Rita Orji, Jim Greer, and Julita Vassileva. 2019. Personalization of Persuasive Technology in Higher Education. Association for Computing Machinery, New York, NY, USA, 336–340. https://doi.org/10.1145/3320435.3320478
[18]
Rita Orji, Julita Vassileva, and Regan L. Mandryk. 2014. Modeling the Efficacy of Persuasive Strategies for Different Gamer Types in Serious Games for Health. User Modeling and User-Adapted Interaction 24, 5 (dec 2014), 453–498. https://doi.org/10.1007/s11257-014-9149-8
[19]
Kiemute Oyibo and Julita Vassileva. 2020. Persuasive Features that Drive the Adoption of a Fitness Application and the Moderating Effect of Age and Gender. Multimodal Technologies and Interaction 4, 2 (2020). https://doi.org/10.3390/mti4020017
[20]
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library. Curran Associates Inc., Red Hook, NY, USA.
[21]
Weiyan Shi, Yu Li, Saurav Sahay, and Zhou Yu. 2021. Refine and Imitate: Reducing Repetition and Inconsistency in Persuasion Dialogues via Reinforcement Learning and Human Demonstration. In Findings of the Association for Computational Linguistics: EMNLP 2021. Association for Computational Linguistics, Punta Cana, Dominican Republic, 3478–3492. https://aclanthology.org/2021.findings-emnlp.295
[22]
Weiyan Shi, Xuewei Wang, Yoo Jung Oh, Jingwen Zhang, Saurav Sahay, and Zhou Yu. 2020. Effects of persuasive dialogues: testing bot identities and inquiry strategies. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1–13.
[23]
Henning Wachsmuth, Nona Naderi, Yufang Hou, Yonatan Bilu, Vinodkumar Prabhakaran, Tim Alberdingk Thijm, Graeme Hirst, and Benno Stein. 2017. Computational Argumentation Quality Assessment in Natural Language. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers. Association for Computational Linguistics, Valencia, Spain, 176–187. https://www.aclweb.org/anthology/E17-1017
[24]
Xuewei Wang, Weiyan Shi, Richard Kim, Yoojung Oh, Sijia Yang, Jingwen Zhang, and Zhou Yu. 2019. Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Florence, Italy, 5635–5649. https://doi.org/10.18653/v1/P19-1566
[25]
Zhongyu Wei, Yang Liu, and Yi Li. 2016. Is This Post Persuasive? Ranking Argumentative Comments in Online Forum. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, Berlin, Germany, 195–200. https://doi.org/10.18653/v1/P16-2032
[26]
Diyi Yang, Jiaao Chen, Zichao Yang, Dan Jurafsky, and Eduard Hovy. 2019. Let’s Make Your Request More Persuasive: Modeling Persuasive Strategies via Semi-Supervised Neural Nets on Crowdfunding Platforms. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, Minneapolis, Minnesota, 3620–3630. https://doi.org/10.18653/v1/N19-1364
[27]
Koichiro Yoshino, Yoko Ishikawa, Masahiro Mizukami, Yu Suzuki, Sakriani Sakti, and Satoshi Nakamura. 2018. Dialogue Scenario Collection of Persuasive Dialogue with Emotional Expressions via Crowdsourcing. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan. https://aclanthology.org/L18-1194
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July 2022
360 pages
ISBN:9781450392075
DOI:10.1145/3503252
- General Chairs:
- Alejandro Bellogin,
- Ludovico Boratto,
- Olga C. Santos,
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- Bart Knijnenburg
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Published: 04 July 2022
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View all- Puspitasari FTyson GHaq EHui PLee L(2023)Ghost Booking as a New Philanthropy ChannelProceedings of the 34th ACM Conference on Hypertext and Social Media10.1145/3603163.3609028(1-11)Online publication date: 4-Sep-2023
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