Keeping Up Appearances: Computational Modeling of Face Acts in Persuasion Oriented Discussions (2020)

Abstract: 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.

## introduction politeness principles, displayed in practice in dayto-day language usage, play a central role in shaping human interaction. formulations of politeness principles are related to basic human needs that are jointly met in and through interaction @xcite @xcite . natural language offers various ways to enact politeness. one of the most influential politeness theories from linguistics is proposed in @xcite , in which a detailed exposition is offered of the individual actions whose cumulative effect results in saving face and losing face, along with a consideration of cost. using this framework, it is possible to analyze how interlocutors make decisions about where and how these devices should be used based on an intricate cost-benefit analysis @xcite . we refer to these component actions here as face acts. the idea of face acts appears quite attractive from a computational standpoint for their potential role in understanding what is "meant" from what is "said" @xcite @xcite . consequently, politeness has been widely researched in various domains of language technologies @xcite @xcite @xcite in addition to foundational work in pragmatics and sociolinguistics @xcite @xcite . however, much prior work modeling politeness reduces the problem to a rating task or binary prediction task, separating polite and impolite behavior. consequently, what the models end up learning is mainly overt markers of politeness or rudeness, rather than the underlying indirect strategies for achieving politeness or rudeness through raising or attacking face, even in the indirect case where no overt markers of rudeness or politeness might be explicitly displayed. in contrast, the main contribution of this work is the investigation of eight major face acts, similar to dialogue acts, including an investigation of their usage in a publicly available corpus of @xcite . in the selected corpus, a persuader (er) is tasked with convincing a persuadee (ee) to donate money to a charity. the nature of the task prompts frequent utilization of face acts in interaction, and thus these face acts are abundantly present in the chosen dataset. we also provide a generalized framework for operationalizing face acts in conversations as well as design an annotation scheme to instantiate these face acts in the context of persuasion conversations ( §2.1, §2.3). we offer the annotations we have added to this public dataset as another contribution of this work ( §2.2). additionally, we develop computational models to identify face acts ( §3.1) as well as construct a latent representation of conversational state to analyze the impact of face acts on conversation success ( §3.2). we achieve 0.6 f1 on classifying face acts ( §5.1), and 0.67 f1 in predicting donation outcome ( §5.2). we observe that the predicted face acts significantly impact the local probability of donation ( §5.3) foot_0 . ## experimental setup we describe the baselines and evaluation metrics here. we present the additional experimental details of our model in appendix table s1 . ## results in this section we put forward the following research questions and attempt to answer the same. q1. how well does bert-higru predict face acts? (section 5.1) q2. how well are we able to predict the outcome of the conversation? (section 5.2) q3. how do individual face acts correlate with donation probability? (section 5.3) ## related work although politeness derailment and politeness evolution in dialogue have been previously investigated in the nlp literature @xcite , the prior work is distinguished from our own in that they do not explicitly model face changes of both parties over time. @xcite utilizes requests annotated for politeness to create a framework specifically to relate politeness and social power. other previous work attempt to computationally model politeness, using politeness as a feature to identify conversations that appear to go awry in online discussions @xcite . previous work has also explored indirect speech acts as potential sources of face-threatening acts through blame @xcite and as face-saving acts in parliamentary debates @xcite . the closest semblance of our work is with @xcite , which builds upon the notion of face provided by @xcite and invents its own set of face acts specifically in the context of "small-talk" conversations. in contrast, our work specifically operationalizes the notion of the positive and negative face of @xcite ; @xcite , which is well established in the pragmatics literature and heavily acknowledged in the nlp community @xcite @xcite . moreover, we focus on analysing the effects of face acts in a "goal-oriented" task like persuasion, where there is an explicit threat or attack on face as opposed to small-talk scenarios, where the goal is building rapport or passing the time. thus our work can be considered to be complementary to the prior work of @xcite and @xcite . it also enables us to draw insights from recent work in persuasion strategy to analyze face act exchanges in persuasion @xcite . ## conclusion and future work in this paper, we present a generalized computational framework based on the notion of face to operationalize face dynamics in conversations. we instantiate these face act exchanges in the context of persuasion and propose a dataset of 296 conversations annotated with face acts. we develop computational models for predicting face acts as well as observe the impact of these predicted face acts on the donation outcome. one important limitation of the current work is the assumption that all face acts have the same intensity/ranking. we seek to rectify this by separating the content and style of these face acts. we also wish to expand the current face framework to a more comprehensive politeness framework that incorporates notions of power and social distance between the interlocutors. we believe that our work may be extended to language generation in chatbots for producing more polite language to mediate face threats. moreover, we intend to instantiate our proposed framework to other domains such as teacher/student conversations and other types of discourse such as social media narratives. face act persuader (er) persuadee (ee) spos+ (i) er praises/promotes the good deeds of stc (i) ee states her preference for other charities (ii) er shows her/ his involvement for stc (ii) ee states that she does good deeds hpos+ (i) er appreciates/praises ee's (i) ee shows willingness to donate generosity or time to discuss the charity (ii) incentives ee to do a good deed. (ii) ee acknowledges the efforts of stc. (iii) empathize/ agree with ee (iii) empathizes/ agrees with er spos-(i) ee apologizes for not donating hpos-(i) er criticizes ee (i) ee doubts/ questions stc or ee (ii) ee is not aware of stc sneg+ (i) rejects donation out-right (ii) cites reason for not donating at all or not donating more. hneg+ (i) er provides ee convenient ways to donate. (ii) er apologizes for inconvenience/ intrusion. (iii) er decreases the amount of donation. hneg-(i) er ask's ee's time/ permission for discussion. (i) ee asks er questions about stc. (ii) er asks ee for donation. (iii) er asks ee to donate more. table s3 : instantiating predicates corresponding to the different face acts in the context of persuasion. read utterance identify the main focus of the utterance does it deal with a negative fta imposition or outcome ? does s say something about herself or the brand that she endorses? does s talk about some aspect of the hearer h? is there an imposition? does it deal with the outcome of the fta? yes yes does s impose a fta on h ? hneg-yes does s increase the amount of fta ? hneg-yes does s decrease the amount of fta? hneg+ yes does s apologize for the fta? hneg+ yes no does s agree to do the fta? is s happy/willing to do the fta? no sneg+ yes yes hpos+ sneg-no yes does s complements herself or states she is a good person? spos+ does s state some of her preferences/ likes or wants? spos+ does s complement the brand or champions it cause? spos+ does s apologize for doing something expected of her or criticize herself? spos-does s criticize the brand or does not believe in it or doubts it? spos-does s complement h for their virtue or actions? no yes does s give an incentive to h to do some potential good? hpos+ hpos+ does s agree/ empathize with h or acknowledge h/ brand's efforts? hpos+ does s criticize or doubt the brand h endorses? does s criticize/ indifferent to h or disagrees with h about something? hpos-hpos-does s offer to assist h or willing to discuss with h?