R es P er: Computationally Modelling Resisting Strategies in Persuasive Conversations (2021)

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 .

## introduction persuasion is pervasive in everyday human interactions. people are often exposed to scenarios that challenge their existing beliefs and opinions, such as medical advice, election campaigns, and advertisements @xcite @xcite . of late, huge strides have been taken by the computational linguistics community to advance research in persuasion. some seminal works include identifying persuasive strategies in text @xcite and conversations @xcite , investigating the * denotes equal contribution interplay of language and prior beliefs on successful persuasion attempts @xcite , and generating persuasive dialogues @xcite . however, a relatively unexplored domain by the community is the investigation of resisting strategies employed to foil persuasion attempts. as succinctly observed by @xcite : "in our daily lives we are struck not by the ease of producing attitude change but by the rarity of it." several works in cognitive and social psychology @xcite have put forward different resisting strategies and the motivations for the same. however, so far, there has not been any attempt to operationalise these strategies from a computational standpoint. we attempt to bridge this gap in our work. we propose a generalised framework, grounded in cognitive psychology literature, for automatically identifying resisting strategies in persuasion oriented discussions. we instantiate our framework on two publicly available datasets comprising persuasion and negotiation conversations to create an annotated corpus of resisting strategies. furthermore, we design a hierarchical sequence modelling framework, that leverages the conversational context to identify resisting strategies automatically. our model significantly outperforms several neural baselines, achieving a competitive macro-f1 score of 0.56 and 0.66 on the persuasion and negotiation dataset, respectively. we refer to our model as resper, which is not only an acronym for resisting persuasion, but also a play on the word esper: a person with extrasensory abilities. the name is apt since we observe that incorporating such resisting strategies could provide additional insight on the outcome of the conversation. in fact, our experiments reveal that the resisting strategies are better predictors of conversation success for the persuasion dataset than the strategies employed by the persuader. we also observe that the buyer's strategies are more influential in negotiating the final price. our findings highlight the asymmetric nature of power roles arising in non-collaborative dialogue scenarios and form motivation for this work. ## related works the use of persuasion strategies to change a person's view or achieve a desired outcome finds several real-world applications, such as in election campaigns @xcite , advertisements @xcite , and mediation @xcite . consequently, several seminal nlp research have focused on operationalising and automatically identifying persuasion strategies @xcite , propaganda techniques @xcite , and negotiation tactics @xcite , as well as the impact of such strategies on the outcome of a task @xcite @xcite . however, there is still a dearth of research from a computational linguistic perspective investigating resisting strategies to foil persuasion. resisting strategies have been widely discussed in literature from various aspects such as marketing @xcite , cognitive psychology @xcite , and political communication @xcite . some notable works include the identification and motivation of commonly-used resisting strategies @xcite , the use of psychological metrics to predict resistance @xcite , and the design of a framework to measure the impact of resistance @xcite . however, these works have mostly relied on qualitative methods, unlike ours, which adopts a data-driven approach. we propose a generalised framework to characterise resisting strategies and employ state-of-the-art neural models to infer them automatically. thus our work can be considered complementary to past research. the closest semblance to our work in nlp literature ties in with argumentation, be it essays @xcite , debates (cano-basave and he, 2016), or discussions on social media platforms @xcite . such works have revolved mostly on analysing argumentative strategies and their effect on others. recently, al @xcite demonstrated that incorporating the personality traits of the resis-tor was influential in determining their resistance to persuasion. such an observation acknowledges the power vested in an individual to resist change to their existing beliefs. our work exhibits significant departure from this because we explicitly characterise the resisting strategies employed by the user. moreover, our work focuses on the general domain of non-collaborative task-oriented dialogues, where several non-factual resisting strategies are observed, making it distinctly different from argumentation @xcite . we assert that focusing on both parties is imperative to get a complete picture of persuasive conversations. ## framework in this section, we describe the datasets, the resisting strategies employed, and the annotation framework to instantiate the strategies. ## methodology in this section, we describe the methodology adopted for inferring the resisting strategies in persuasion dialogues and how they can be leveraged to determine the dialogue's outcome. ## experiments in this section, we describe the baselines and evaluation metrics. we present the experimental details of our model in ## results in this section, we answer the following : q1. how well does resper identify resisting strategies for persuasion and negotiation? q2. are resisting strategies good predictors of conversation success? what insights can one glean from the results? ## conclusion we present a generalised computational framework grounded in cognitive psychology to operationalise resisting strategies employed to counter persuasion. we identify seven distinct resisting strategies that we instantiate on two publicly available corpora comprising persuasion and negotiation conversations. we adopt a hierarchical sequence labelling architecture to infer the resisting strategies automatically and observe that our model achieves competitive performance for both datasets. furthermore, we examine the interplay of resisting strategies in determining the final conversation outcome, which corroborates with previous findings. in the future, we would like to explore better models to encode the strategy information and apply our framework to improve personalised persuasion and negotiation dialogue systems. we would also like to study the influence of other confounding factors such as power dynamics on the outcomes of conversations featuring resisting strategies.