Title: ResPer: Computationally Modelling Resisting Strategies in Persuasive Conversations
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 this https URL. more »« less
Dutt, Ritam; Joshi, Rishabh; Rose, Carolyn(
, Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP))
null
(Ed.)
The notion of face refers to the public self-image of an individual that emerges both from the individual’s own actions as well as from the interaction with others. Modeling face and understanding its state changes throughout a conversation is critical to the study of maintenance of basic human needs in and through interaction. Grounded in the politeness theory of Brown and Levinson (1978), we propose a generalized framework for modeling face acts in persuasion conversations, resulting in a reliable coding manual, an annotated corpus, and computational models. The framework reveals insights about differences in face act utilization between asymmetric roles in persuasion conversations. Using computational models, we are able to successfully identify face acts as well as predict a key conversational outcome (e.g. donation success). Finally, we model a latent representation of the conversational state to analyze the impact of predicted face acts on the probability of a positive conversational outcome and observe several correlations that corroborate previous findings.
Identifying persuasive speakers in an adversarial environment is a critical task. In a national election, politicians would like to have persuasive speakers campaign on their behalf. When a company faces adverse publicity, they would like to engage persuasive advocates for their position in the presence of adversaries who are critical of them. Debates represent a common platform for these forms of adversarial persuasion. This paper solves two problems: the Debate Outcome Prediction (DOP) problem predicts who wins a debate while the Intensity of Persuasion Prediction (IPP) problem predicts the change in the number of votes before and after a speaker speaks. Though DOP has been previously studied, we are the first to study IPP. Past studies on DOP fail to leverage two important aspects of multimodal data: 1) multiple modalities are often semantically aligned, and 2) different modalities may provide diverse information for prediction. Our M2P2 (Multimodal Persuasion Prediction) framework is the first to use multimodal (acoustic, visual, language) data to solve the IPP problem. To leverage the alignment of different modalities while maintaining the diversity of the cues they provide, M2P2 devises a novel adaptive fusion learning framework which fuses embeddings obtained from two modules -- an alignment module that extracts shared information between modalities and a heterogeneity module that learns the weights of different modalities with guidance from three separately trained unimodal reference models. We test M2P2 on the popular IQ2US dataset designed for DOP. We also introduce a new dataset called QPS (from Qipashuo, a popular Chinese debate TV show) for IPP - we plan to release this dataset when the paper is published. M2P2 significantly outperforms 3 recent baselines on both datasets.
Bao, Jiajun; Wu, Junjie; Zhang, Yiming; Chandrasekharan, Eshwar; Jurgens, David(
, The Web Conference)
Online conversations can go in many directions: some turn out poorly due to antisocial behavior, while others turn out positively to the benefit of all. Research on improving online spaces has focused primarily on detecting and reducing antisocial behavior. Yet we know little about positive outcomes in online conversations and how to increase them—is a prosocial outcome simply the lack of antisocial behavior or something more? Here, we examine how conversational features lead to prosocial outcomes within online discussions. We introduce a series of new theory-inspired metrics to define prosocial outcomes such as mentoring and esteem enhancement. Using a corpus of 26M Reddit conversations, we show that these outcomes can be forecasted from the initial comment of an online conversation, with the best model providing a relative 24% improvement over human forecasting performance at ranking conversations for predicted outcome. Our results indicate that platforms can use these early cues in their algorithmic ranking of early conversations to prioritize better outcomes.
Martin, R. C.; Lang, C. K.; Liu, S.-N. C.; Sandoval, C. L.; Bergman, M. E.; & Froyd, J. E.(
, 2019 ASEE Annual Conference & Expositio)
The Improving Student Experiences to Increase Student Engagement (ISE-2) grant was awarded to Texas A&M University by the National Science Foundation, through EEC-Engineering Diversity Activities (Grant No. 1648016) with the goal of increasing student engagement and retention in the College of Engineering. The major component of the intervention was a faculty development program aimed to increase active learning, improve classroom climates, and decrease implicit bias and deficit thinking. Faculty teaching first- and second-year Engineering courses participated in the ISE-2 faculty development program, with the first cohort (n = 10) in Summer 2017 and the second cohort (n = 5) in Summer 2018. This paper describes the content of each of these components of the faculty development program and provides access to a Google drive (still in development at the time of the abstract) with resources for others to use.
The faculty development program consisted of three workshops, a series of coffee hour conversations, and two deliverables from the participants (a teaching plan at the conclusion of the summer training and a final reflection a year following the training). Anchoring the program was a framework for teaching in a diverse classroom (Adams & Love, 2009). Workshop 1 (early May) consisted of an overview of the ISE-2 program. During the first workshop, faculty were introduced to social cognitive biases and the behaviors that result from these biases. During this workshop, the ISE-2 team shared findings from a climate study related to the classroom experiences of students at the College of Engineering. Workshop 2 (mid-May) focused on how undergraduate students learn, provided evidence for the effectiveness of active learning strategies, and exposed faculty participants to active learning strategies. Workshop 3 (early August) integrated the material from the first two workshops as faculty participants prepared to apply the material to their own teaching. Prior to each workshop, the faculty participants were provided with pre-workshop readings to familiarize them with some of the content matter. Coffee hour conversations—informal discussions between the participating faculty and the ISE-2 team centered around a teaching topic selected by participants—were conducted on a near-weekly basis between the second and third workshops. Handouts and worksheets were provided at each coffee hour and served to guide the coffee hour discussions. After the last workshop but before the Fall semester, faculty participants created a teaching plan to incorporate what they learned in the ISE-2 program into their own teaching. At the end of the academic year, the faculty participants are tasked with completing a final reflection on how ISE-2 has affected their teaching in the previous academic year.
In this paper, we will report the content of each of the three workshops and explain how these workshops are related to the overarching goals of the ISE-2 program. Then, we will discuss how each of the coffee hour conversation topics complement the material covered in the workshops. Lastly, we will explore the role of the teaching plans and final reflections in changing instructional practices for faculty.
Global sea level rise (SLR) may present the most urgent climate change adaptation challenge facing coastal communities today. The direction is clear, impacts are manifesting now, and the pace of rise is likely to accelerate. As a result, many coastal communities have begun planning their adaptation response and some are quite far along in the process.
At the same time, evolving science provides new observations, models, and understanding of land-ocean dynamics that can increase clarity while also in many ways increase uncertainty about the scope, timing, and regional nature of SLR. The planning, design, and construction of water infrastructure has a relatively long timeline (up to 30 years), and thus the evolution of scientific knowledge presents challenges for communities already planning for SLR based on previous information. When does science become actionable for decision-makers? Are there characteristics or thresholds that could cause communities decide to move from one set of scenarios to another, or change approaches altogether?
This talk focuses on two important studies different in kind but dominating the conversation about SLR adaptation planning today. First, DeConto and Pollard (2016) have suggested significantly higher upper end projections for Antarctic ice sheet melt, which increase both global and regional SLR above most previously assumed upper limits. Second, probabilistic projections using model output and expert elicitation as presented in Kopp et al (2014) are increasingly appearing in federal reports and planning-related documents. These two papers are pushing the boundaries of the science-to-planning interface, while the application of this work as actionable science is far from settled.
This talk will present the outcome of recent conversations among our diverse author team. The authors are engaged in SLR planning related contexts from many angles and perspectives and include the aforementioned Kopp and DeConto as well as representatives of the City of San Francisco, Army Corps of Engineers, Environmental Protection Agency, and engineering consultant community. Attendees of this session will hear a presentation demonstrating co-production in process, including topics about which the authors have and have not agreed upon to date, with some attention to next steps in the process.
Ritam Dutt, Sayan Sinha. ResPer: Computationally Modelling Resisting Strategies in Persuasive Conversations. Retrieved from https://par.nsf.gov/biblio/10295167. Proceedings of the European Chapter of the Association for Computational Linguistics .
Ritam Dutt, Sayan Sinha. ResPer: Computationally Modelling Resisting Strategies in Persuasive Conversations. Proceedings of the European Chapter of the Association for Computational Linguistics, (). Retrieved from https://par.nsf.gov/biblio/10295167.
Ritam Dutt, Sayan Sinha.
"ResPer: Computationally Modelling Resisting Strategies in Persuasive Conversations". Proceedings of the European Chapter of the Association for Computational Linguistics (). Country unknown/Code not available. https://par.nsf.gov/biblio/10295167.
@article{osti_10295167,
place = {Country unknown/Code not available},
title = {ResPer: Computationally Modelling Resisting Strategies in Persuasive Conversations},
url = {https://par.nsf.gov/biblio/10295167},
abstractNote = {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 this https URL.},
journal = {Proceedings of the European Chapter of the Association for Computational Linguistics},
author = {Ritam Dutt, Sayan Sinha},
editor = {null}
}
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