@inproceedings{chen-etal-2022-seamlessly,
title = "Seamlessly Integrating Factual Information and Social Content with Persuasive Dialogue",
author = "Chen, Maximillian and
Shi, Weiyan and
Yan, Feifan and
Hou, Ryan and
Zhang, Jingwen and
Sahay, Saurav and
Yu, Zhou",
editor = "He, Yulan and
Ji, Heng and
Li, Sujian and
Liu, Yang and
Chang, Chua-Hui",
booktitle = "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)",
month = nov,
year = "2022",
address = "Online only",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.aacl-main.31/",
doi = "10.18653/v1/2022.aacl-main.31",
pages = "399--413",
abstract = "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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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Seamlessly Integrating Factual Information and Social Content with Persuasive Dialogue
%A Chen, Maximillian
%A Shi, Weiyan
%A Yan, Feifan
%A Hou, Ryan
%A Zhang, Jingwen
%A Sahay, Saurav
%A Yu, Zhou
%Y He, Yulan
%Y Ji, Heng
%Y Li, Sujian
%Y Liu, Yang
%Y Chang, Chua-Hui
%S 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)
%D 2022
%8 November
%I Association for Computational Linguistics
%C Online only
%F chen-etal-2022-seamlessly
%X 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.
%R 10.18653/v1/2022.aacl-main.31
%U https://aclanthology.org/2022.aacl-main.31/
%U https://doi.org/10.18653/v1/2022.aacl-main.31
%P 399-413
Markdown (Informal)
[Seamlessly Integrating Factual Information and Social Content with Persuasive Dialogue](https://aclanthology.org/2022.aacl-main.31/) (Chen et al., AACL-IJCNLP 2022)
ACL
- Maximillian Chen, Weiyan Shi, Feifan Yan, Ryan Hou, Jingwen Zhang, Saurav Sahay, and Zhou Yu. 2022. Seamlessly Integrating Factual Information and Social Content with Persuasive Dialogue. In 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), pages 399–413, Online only. Association for Computational Linguistics.