@inproceedings{dutt-etal-2021-resper,
title = "{R}es{P}er: Computationally Modelling Resisting Strategies in Persuasive Conversations",
author = "Dutt, Ritam and
Sinha, Sayan and
Joshi, Rishabh and
Chakraborty, Surya Shekhar and
Riggs, Meredith and
Yan, Xinru and
Bao, Haogang and
Rose, Carolyn",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.7",
doi = "10.18653/v1/2021.eacl-main.7",
pages = "78--90",
abstract = "Modelling persuasion strategies as predictors of task outcome has several real-world applications and has received considerable attention from the computational linguistics community. However, previous research has failed to account for the resisting strategies employed by an individual to foil such persuasion attempts. Grounded in prior literature in cognitive and social psychology, we propose a generalised framework for identifying resisting strategies in persuasive conversations. We instantiate our framework on two distinct datasets comprising persuasion and negotiation conversations. We also leverage a hierarchical sequence-labelling neural architecture to infer the aforementioned resisting strategies automatically. Our experiments reveal the asymmetry of power roles in non-collaborative goal-directed conversations and the benefits accrued from incorporating resisting strategies on the final conversation outcome. We also investigate the role of different resisting strategies on the conversation outcome and glean insights that corroborate with past findings. We also make the code and the dataset of this work publicly available at \url{https://github.com/americast/resper}.",
}
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<abstract>Modelling persuasion strategies as predictors of task outcome has several real-world applications and has received considerable attention from the computational linguistics community. However, previous research has failed to account for the resisting strategies employed by an individual to foil such persuasion attempts. Grounded in prior literature in cognitive and social psychology, we propose a generalised framework for identifying resisting strategies in persuasive conversations. We instantiate our framework on two distinct datasets comprising persuasion and negotiation conversations. We also leverage a hierarchical sequence-labelling neural architecture to infer the aforementioned resisting strategies automatically. Our experiments reveal the asymmetry of power roles in non-collaborative goal-directed conversations and the benefits accrued from incorporating resisting strategies on the final conversation outcome. We also investigate the role of different resisting strategies on the conversation outcome and glean insights that corroborate with past findings. We also make the code and the dataset of this work publicly available at https://github.com/americast/resper.</abstract>
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%0 Conference Proceedings
%T ResPer: Computationally Modelling Resisting Strategies in Persuasive Conversations
%A Dutt, Ritam
%A Sinha, Sayan
%A Joshi, Rishabh
%A Chakraborty, Surya Shekhar
%A Riggs, Meredith
%A Yan, Xinru
%A Bao, Haogang
%A Rose, Carolyn
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F dutt-etal-2021-resper
%X Modelling persuasion strategies as predictors of task outcome has several real-world applications and has received considerable attention from the computational linguistics community. However, previous research has failed to account for the resisting strategies employed by an individual to foil such persuasion attempts. Grounded in prior literature in cognitive and social psychology, we propose a generalised framework for identifying resisting strategies in persuasive conversations. We instantiate our framework on two distinct datasets comprising persuasion and negotiation conversations. We also leverage a hierarchical sequence-labelling neural architecture to infer the aforementioned resisting strategies automatically. Our experiments reveal the asymmetry of power roles in non-collaborative goal-directed conversations and the benefits accrued from incorporating resisting strategies on the final conversation outcome. We also investigate the role of different resisting strategies on the conversation outcome and glean insights that corroborate with past findings. We also make the code and the dataset of this work publicly available at https://github.com/americast/resper.
%R 10.18653/v1/2021.eacl-main.7
%U https://aclanthology.org/2021.eacl-main.7
%U https://doi.org/10.18653/v1/2021.eacl-main.7
%P 78-90
Markdown (Informal)
[ResPer: Computationally Modelling Resisting Strategies in Persuasive Conversations](https://aclanthology.org/2021.eacl-main.7) (Dutt et al., EACL 2021)
ACL