PEPDS : A Polite and Empathetic Persuasive Dialogue System for Charity Donation (2022)

Abstract: Persuasive conversations for a social cause often require influencing other person’s attitude or intention that may fail even with compelling arguments. The use of emotions and different types of polite tones as needed with facts may enhance the persuasiveness of a message. To incorporate these two aspects, we propose a polite, empathetic persuasive dialogue system (PEPDS). First, in a Reinforcement Learning setting, a Maximum Likelihood Estimation loss based model is fine-tuned by designing an efficient reward function consisting of five different sub rewards viz. Persuasion, Emotion, Politeness-Strategy Consistency, Dialogue-Coherence and Non-repetitiveness. Then, to generate empathetic utterances for non-empathetic ones, an Empathetic transfer model is built upon the RL fine-tuned model. Due to the unavailability of an appropriate dataset, by utilizing the PERSUASIONFORGOOD dataset, we create two datasets, viz. EPP4G and ETP4G. EPP4G is used to train three transformer-based classification models as per persuasiveness, emotion and politeness strategy to achieve respective reward feedbacks. The ETP4G dataset is used to train an empathetic transfer model. Our experimental results demonstrate that PEPDS increases the rate of persuasive responses with emotion and politeness acknowledgement compared to the current state-of-the-art dialogue models, while also enhancing the dialogue’s engagement and maintaining the linguistic quality.

## introduction a persuasive message can be initially analyzed by distinguishing between the cause or stimulus the persuadee is being persuaded for and the associated attitude shown during persuasion. the message is first presented to the persuadee; s/he pays attention to it and comprehends its contents. a persuadee's response depends partly on the information provided in the message and how one perceives or interprets it. empathy plays a crucial role in mediating the persuasive effects as it evokes cognitive and emotional processing conducive to persuasion. similarly, use of different polite tones as per context may establish a better connection with the persuadee and engage them for a longer time in the ongoing conversation. therefore, for persuasion to be effective, cause or stimulus of persuasion can be encoded with empathy and politeness, ensuring interactiveness, empathetic connection, right tone as well as user engagement in a persuasive message. a persuasive message consists of some form of directives, hence having the potential for disagreement or dissatisfaction thus may threaten the face of recipients. hence, messages employing different politeness strategies such as positive politeness and negative politeness as per user's mood and message content may emphasize users' freedom to get persuaded or not. for an example, in figure 1 , the strike through response is persuasive but also consists of forceful language. whereas, the green box response connects with the user empathetically and also gives him/her freedom of choice by adopting positive politeness strategy (i.e. by using non-imposing, face-saving language). further, to acknowledge underlying emotion in the content an empathetic transfer model can transfer the nonempathetic messages to empathetic messages as shown in orange box of figure 1 . paucity of available data poses a challenge to model a persuasive dialogue system in a supervised learning (sl) setting. further, modelling persuasion considering different attributes such as politeness and empathy brings a greater challenge due to its different dynamics with different users. therefore, a model which can learn from user interactions itself in an ongoing dialogue is required. recently, researchers had widely used reinforcement learning (rl) to reinforce dialogue agents to approximate better policy as per user's feedbacks @xcite @xcite @xcite . hence, to induce both, a consistent politeness-strategy and empathy, we utilize goodness of both the frameworks i.e. rl and sl, and propose a novel polite-empathetic persuasive dialogue system (pepds). to ensure persuasiveness, politeness-strategy consistency, right emotion, dialogue coherence and non-repetitiveness, a maximum likilehood estimation loss (mle) language based model is fine-tuned with an rl loss considering five rewards viz. persuasion, emotion, politeness-strategy consistency, dialogue-coherence and repetitiveness. in case the generated persuasive messages are found to be non-empathetic, a sequence-to-sequence (seq2seq) based style transfer model is employed to make them empathetic. in order to achieve this goal, we prepare a new dataset named as etp4g from the epp4g dataset. to obtain persuasion, emotion and politeness-strategy consistency reward feedbacks, first, we annotate persuasionforgood (p4g) dataset @xcite with the required emotion and politeness-strategy labels, and obtain a new dataset named as epp4g. then, considering these three aspects, we fine-tune three robertalarge @xcite based respective classifiers. recently, to generate persuasive responses, an mle-loss based language model is fine-tuned by @xcite with an rl-loss without using any user simulators. to persuade the persuadee, they penalized the generation of repetitive and inconsistent utterances. our work dif-fers from them in three aspects. firstly, we consider new aspects of politeness-strategy consistency and emotion acknowledgement to force the dialogue agent to be consistent as well as empathetic. second, a new reward function is designed to ensure right persuasion strategy, emotion acknowledgement, politeness-strategy consistency, dialogue-coherence in an ongoing dialogue and non-repetition of similar responses. third, to connect with the user empathetically, we transfer the non-empathetic responses to empathetic ones. lastly, we perform automatic and human evaluation to assess the persuasiveness, politeness-strategy consistency, empathy, responselength, fluency, adequacy, consistency and nonrepetitiveness of the generated responses for our proposed pepds. following are the key contributions we present in this paper: ## related work recent research on personalised conversational agents shows that incorporating various humanoriented conversational strategies can have a significant impact on the user responses and make the conversations more engaging @xcite @xcite @xcite . these dialogue agents can better acknowledge the user's state to adapt themselves as per user's need. for instance, bertero et al. (2016) tries to recognize user emotions in real-time in an interactive dialogue system. shi and yu (2018) builds an effective dialogue system considering user's sentiment information, whereas firdaus et al. (2020) utilizes multimodal information to generate sentiment and emotion controlled responses. golchha et al. (2019) induces courteous behaviour in customer care responses. similarly, mishra et al. (2022) designed three politeness based rewards to reinforce polite responses in an ongoing task-oriented dialogue. due to subtle dependency between these user-targeted personalization techniques such as politeness-strategy, empathy or sentiment, and persuasion, we focus on incorporating politeness-strategy consistency and empathy to generate more persuasive and engaging utterances. historically, different persuasion models have been proposed, such as petty and cacioppo's elaboration likelihood model (elm) (petty and cacioppo, 1986) and friestad and wright's persuasion knowledge model (pkm) @xcite . this indicates that a person's persuasion may depend on several factors, such as the content of the message, context, common sense knowledge and scientific knowledge. similarly, @xcite states that personal factor consideration with informative content can enhance the quality of persuasive messages. @xcite ; sparks and areni @xcite point out that the language employed in persuasive messages can impact both perceptions of the persuader as well as the message conveyed. similarly, @xcite ; @xcite in their politeness theory points out that persuasive messages in the absence of indirect or face-saving language may commensurate threat in the face of recipients. recent research studies have started focusing on building persuasive dialogue agents by considering different aspects, such as persuasion strategies @xcite and resistive strategies @xcite . @xcite conducted an online study considering 790 participants to check if they can be persuaded or not by a chatbot. further, the availability of transformer based pre-trained language models has led the researchers to train style transfer even in the absence of parallel data @xcite @xcite @xcite @xcite . our current method of transferring non-empathetic utterance to empathetic is based on the prior technique proposed in @xcite . recently, two mle-loss based language mod-els -one for persuadee and one for persuader are jointly trained to generate persuasive responses @xcite . @xcite fine-tunes a language model in rl-setting to persuade with non-repetitive and consistent responses. in contrast to the fact that persuasion encompasses a vast domain space with different associated attitudes, these research studies either concentrated on predicting persuasion strategy or generating persuasive responses alone. persuasion alone cannot ensure user engagement; a dialogue agent should be able to adapt to different associated attitudes as per rapport built with the user. therefore, our work here focuses on adapting different politeness strategies as per user attitudes and generating empathetic and engaging persuasive dialogues. to the best of our knowledge, no previous study has incorporated politeness strategies to build a persuasive dialogue system. ## methodology a persuasive dialogue @xmath0, u ee 1 , u er 2 , u ee 2 , ..., u er t -1 , u ee t -1 } consists of t -1-turns, where u er i and u ee i represents the persuader's and persuadee's utterance, respectively, at i t h turn (1 < @xmath1. considering this ongoing dialogue as a context, our goal is to generate an adequate dialogue coherent persuader's utterance u ee @xmath2, t 2 , ..., t r } at turn t with r number of tokens. to achieve this goal, first, an mle-loss based language model is fine-tuned with an rl-loss by designing an efficient reward function to generate rich emotion acknowledged and politeness-strategy consistent persuasive responses. on generating the non-empathetic utterances, it is passed through an empathetic transfer model to transfer to an empathetic utterance. ## datasets and experiments the datasets utilised in our studies are introduced in section 4.1. in section 4.2, implementation details of the proposed rl-based system are discussed. due to space constraints, the classifiers' and empathetic transfer model's implementation details are provided in section a.3 of the appendix. finally, section 4.3 describes the specifics of automatic and human evaluation metrics. ## results and analysis we first analyse the results of our sub-modules used in our proposed system viz. all the four classifiers and the empathetic transfer model. then, we analyse our proposed system by comparing it with two baselines: ardm (mle loss based model) @xcite and rfi (dialgail with response filter and response imitator) @xcite . automatic and human evaluation results of our proposed system are shown in ## conclusion in persuasive conversations, even responses with factual arguments and the right cause may not be able to persuade due to a lack of polite and empathetic tone. therefore, to ensure these two aspects in persuasive messages, we first fine-tune an mle loss language model with an rl-loss function consisting of five rewards viz. persuasion, emotion, politeness-strategy consistency, dialoguecoherence, and non-repetitiveness. then generated non-empathetic utterances are transferred using an auto-regressive seq2seq model to empathetic utterances. detailed empirical evaluation concerning both automatic and human evaluation metrics demonstrate that our proposed model, pepds can achieve state-of-the-art performance compared to the existing baselines and can retain both the aspects of emotion and politeness-strategy consistency at par in an ongoing dialogue. our results also conclude that adding the empathetic transfer model helps the proposed model better facilitate empathy in persuasive responses. we would like to see more into the personalization aspects in the future to model persuasion, such as likeness, authority, demography, etc.