Persona or Context? Towards Building Context adaptive Personalized Persuasive Virtual Sales Assistant (2022)

Abstract: Task-oriented conversational agents are gaining immense popularity and success in a wide range of tasks, from flight ticket booking to online shopping. However, the existing systems presume that end-users will always have a pre-determined and servable task goal, which results in dialogue failure in hostile scenarios, such as goal unavailability. On the other hand, human agents accomplish users’ tasks even in a large number of goal unavailability scenarios by persuading them towards a very similar and servable goal. Motivated by the limitation, we propose and build a novel end-to-end multi-modal persuasive dialogue system incorporated with a personalized persuasive module aided goal controller and goal persuader. The goal controller recognizes goal conflicting/unavailability scenarios and formulates a new goal, while the goal persuader persuades users using a personalized persuasive strategy identified through dialogue context. We also present a novel automatic evaluation metric called P ersuasiveness Me asurement R ate ( PMeR ) for quantifying the persuasive capability of a conversational agent. The obtained improvements (both quantitative and qualitative) firmly establish the superiority and need of the proposed context-guided, personalized persuasive virtual agent over existing traditional task-oriented virtual agents. Furthermore, we also curated a multi-modal persuasive conversational dialogue corpus annotated with intent, slot, sentiment, and dialogue act for e-commerce domain.

## introduction conversational artificial intelligence is gaining popularity and adoption in various fields, owing to its effective task handling and scalability aspects @xcite @xcite . in task-oriented dialogue systems, the primary objective of both users and agents is successful task completion @xcite . our proposed work is relevant to task-oriented dialogue settings where the proposed agent aims to assist end-users in accomplishing a task. in real life, when a human sales agent fails to fulfill consumers' proposed task requirements, he/she finds a very similar goal and attempts to influence them toward the new goal. furthermore, end-users prefer to explore and obtain a servable goal by overlooking a little mismatch in many cases. however, existing dialogue systems @xcite @xcite terminate conversations in such adversarial situations. an illustration has been shown in figure 1 . while the traditional assistant simply terminates the conversation in the goal unavailability situation, the proposed assistant attempts to serve a very similar phone and persuade the user using context-guided persuasive appeal. persuasion is a subjective concern that largely depends on the persuadee's personality, context, and persuasion target aspect @xcite . even the same persuasion target/strategy may not successfully persuade the same user in two different scenarios. hence, context-driven personalized persuasion appears to be more effective than a fixed/static persuasion strategy for resolving goal-shifting conflicts. thus, we aim to build a model that leverages both dialogue context and user persona to determine an appropriate persuasion strategy. in many domains, such as e-commerce and fashion, end-users find it challenging to describe some of their task specifications, for example, glacier white color and flip-style phone, through text, rendering multi-modality a necessity rather than an additional feature. therefore, an agent that can handle both textual and visual information can certainly increase users' satisfaction and hence, the usefulness of the agent. there are only a few works in the dialogue lit-hi, i am looking for a new phone, can you please assist me in purchasing a new phone ? sure; please let me know your main requirements / specifications. i trust only nokia brand, so please show me a nokia brand mobile. it should have at least 16 mp primary camera and large display. fine, do you have any more specifications ? no, please show a phone having mentioned features. great, you should buy lumia 1520 by nokia, which matches with your requirement. what would be its display resolution ? its display resolution will be is 367 ppi. how much does it have ? it contains 2 gb ram and 16 gb internal ram. oh, ram is very less, it will not work for me. i need at least 4 gb ram. thank you hi, i am looking for a new phone, can you please assist me in purchasing a new phone ? sure, please let me know your main requirements / specifications. i trust only nokia brand, so please show me a nokia brand mobile. it should have at least 16 mp primary camera and large display. fine, do you have any more specifications ? no, please show a phone having mentioned features. great, you should buy lumia 1520 by nokia, which matches with your requirement. how much ram does it have ? i do not like this color,,please show something in this color. it contains 2 gb ram and 16 gb internal ram. oh, ram is very less, it will not work for me. i need at least 4 gb ram. let me find the best phone for you; you can buy nokia 6. it contains 3 gb ram. it will be an excellent phone for you. but ram is 3 gb only, i need 4 gb ram phone. it might be a bit less but considering your aspects, it is the best phone in nokia. the phone is one of the highly-rated mobiles of the nokia brand, the most reliable brand. that is true. it would be really nice if i can get 4 gb ram phone. nokia brand, which ensures its outstanding quality. many other brand phones with the same quantity do not perform equally well for a long time. you should buy this phone without a second thought. yeah, right,. please book it for me great, the phone has been booked for you. thank you for shopping with us. ## related work the proposed work is mainly relevant to the three research areas: recommendation system, persuasive dialogue system, and user adaptive virtual assistants. in the subsequent paragraphs, we have summarized each area's recent and relevant works. recommendation systems people's likes and dislikes can change with time and context. inspired by the idea, this work @xcite formulated recommendation as a dialogue problem where the agent interacts with users to collect context and recommends them accordingly. in @xcite , the authors proposed a neural-based response generation system that generates a template and fills in a recommended item based on discourse. persuasive dialogue systems in @xcite , the authors developed a persuasive dialogue conversation corpus where both persuadee and persuader persuade each other to donate to a non-profit organization. in @xcite , the authors investigated the impact of end users' perceived identity of the chatbot on persuasion outcome (donation probability) using a wizard-of-oz study. the findings imply that end-users who perceive bot identity as human have a much higher likelihood of donating. in @xcite , the authors have proposed a multimodal persuasive virtual assistant for handling goal unavailability. nevertheless, the assistant does not utilize dialogue context for selecting an appropriate strategy; thus, it always persuades an end-user with his/her personal attributes. user adaptive virtual assistant in @xcite , the authors investigated the role of user sentiment in dialogue policy learning and proposed a user sentiment adaptive virtual agent trained using a combination of task and sentiment-based rewards. the work @xcite proposed a multimodal (textual and visual) task-oriented dialogue agent, which firmly suggests that multi-modal data can also enhance task success rate and dialog turns significantly, in addition to user convenience. in @xcite , authors proposed a style (gender, sentiment, and emotion) aware neural response generation method, which significantly outperforms existing baselines. ## dataset we first extensively investigated existing benchmark dialogue corpora, and the summary is presented in ## proposed methodology the architecture of the proposed end-to-end personalized persuasive multi-modal dialogue (ppmd) system is shown in figure 3 . the primary parts are as follows: natural language understanding (nlu), dialogue management (dm), and natural language generation (nlg). the key novelties lie in the dialogue management module. the proposed architecture incorporates the following three modules in traditional dialogue manager to strengthen its capability to deal with dynamic and goal unavailability scenarios: (a) goal controller, (b) goal persuader, and (c) dialogue policy learning with a cumulative reward. the goal controller monitors end-users' task goals and detects goal conflicting/unavailability conditions using end-user sentiment and the underlying serving database. in conflicting scenarios, it formulates a new goal and triggers the goal persuader to persuade users by employing a personalized persuasive strategy. we incorporate three different reward models in dialogue policy learning, namely task-based, sentiment-based, and personabased, to simultaneously reinforce task-specific, user-adaptive, and personalized behavior. the detailed working methodologies of each module have been explained in the subsequent sections. ## results and discussion we have utilized all the most popular evaluation metrics, such as success rate, average dialogue length, and average reward, for evaluating the performance of a task-oriented virtual assistant @xcite @xcite . furthermore, we have also proposed a novel automatic evaluation metric called persuasion measure rate (pmer) for measuring the persuasiveness aspects of conversational systems. the metric is defined as follows: these three components are measured as follows: i. persuasiveness (p t ): persuasion success is a very subjective concern, and it depends on a variety of factors. personalization is one of the most influential factors in any persuasive environmental setting. thus, the agent gets a score of +p if the agent persuades users using an attribute (camera quality) aligned with their persona (profile-photographer); otherwise, 0. ii. users' sentiment adaptiveness (s t ): users' sentiment implicitly conveys the effectiveness on agent behavior, including persuasive effort and information about their expectations. hence, we account the factor for measuring persuasiveness success as follows: -s if user sentiment is negative at t th time step otherwise 0. iii. persuasion adequateness(s t ): the persuasion adequateness will be +p success if the agent persuades user successfully; -p f ail if the agent fails to persuade, otherwise 0. the baselines are as follows: i. random agent: the agent randomly selects an action (response) from the agent's action space without considering a dialogue context. ii. rule agent: the agent requests a series of information (slot) and attempts to serve a goal from the extracted information only. iii. dialogue agent without persuasion (dawop): the agent does not persuade end-users in case of goal unavailability scenarios. iv. dialogue agent with persona aware persuasion (dawpp) in the case of goal unavailability, the dawpp agent always persuades end-users using a persona-aware persuasive strategy without considering dialogue context. v. personalized persuasive multi-modal dialogue (ppmd) agent with ddqn: it is the proposed dialogue agent where policy has been trained through ddqn. the performance of the joint intent and slot module is reported in the performances of different baselines and the proposed dialogue agents (average of five iteraobtained by the baselines and the proposed agent have been reported in figure 4 (left side). analysis the detailed analysis leads to the following observations: i. we observed that the persuasive strategy classifier employs both current utterances and previous utterances of the user to determine an appropriate strategy more successfully ( ## conclusion virtual assistants are rapidly becoming our companions in completing various tasks, such as ticket reservations and online shopping. in this work, we proposed and built a novel end-to-end personalized persuasive multi-modal dialogue (ppmd) agent that includes a persuasive strategy identifier, goal controller, and goal persuader module for dealing with goal unavailability situations effectively. we also propose an automatic evaluation metric called pmer that measures the persuasiveness aspect of a conversational system. the obtained results and comparisons with different baselines firmly establish the role of dynamic and contextdriven personalized persuasive dialogue framework over non-persuasive and fixed strategy-based persuasive dialogue systems. in the future, we would like to investigate the role of inter-relations among different persuasion strategies and model the information using a graph neural network for effectively persuading end-users with multiple relevant persuasion strategies.