Caren Al Anaissy
email: alanaissy@cril.fr

Srdjan Vesic
email: vesic@cril.fr

Nathalie Nevejans
email: nathalie.nevejans@univ-artois.fr

Towards Ethical Argumentative Persuasive Chatbots

Keywords: Persuasive chatbots, Ethics of persuasive technology, Computational argumentation

Argumentative persuasive technologies are technologies that use argumentation in order to persuade the persuadee to believe in something or not, which can later lead the persuadee to perform an action or not. The use of such tools opens numerous ethical considerations. In this paper, we survey the literature on persuasion that might be useful for argumentative persuasive chatbots, we cover the existing legal framework and ethical principles and we critically analyze the new proposal for a regulation on artificial intelligence of the European Commission. We also show how to use argumentation to enhance explainability and transparency of the persuasion systems. We propose to show the graphical representation of the arguments used during the persuasion to the user at the end of the dialogue, containing the relations between the arguments (attacks, supports), their origin (source), who uttered them (i.e. the machine or the human participant) and the persuasive methods employed. Our approach has several benefits. Namely, it makes the system more transparent and enhances the human understanding of the system, which is a benefit per se. Furthermore, the fact that the system is transparent increases the trust of the user, which (apart from being one of the goals of AI in general) can increase the chance that the user is persuaded by the system. Finally, the user can give a feedback on the presented arguments (e.g. how much they believe the arguments are ethical), which can be later used to improve the persuasion system.

Introduction

Persuasion aims to change people's attitudes or behaviours [START_REF] Fogg | Persuasive technology: using computers to change what we think and do[END_REF]. Persuasive technologies are very powerful tools because of the simulation they can create. They help shaping the perception and interpretation of reality to users by amplifying specific perceptions and reducing others. They also help shaping the users' actions in reality by encouraging specific forms of actions and discouraging others [START_REF] Verbeek | Persuasive technology and moral responsibility toward an ethical framework for persuasive technologies[END_REF]. Persuasive technologies can also convey social presence when interacting with the user. Many studies have identified persuasive strategies that can be used to influence users and enhance persuasion. Argumentative persuasive chatbots deploy argumentation graphs as their knowledge base. Some of the chatbots even use argumentation semantics for decision-making problems [START_REF] Bistarelli | A chatbot extended with argumentation[END_REF]. However, the persuasive acts employed by these chatbots might sometimes be considered morally and ethically unacceptable. Note that the chatbot itself cannot take responsibility for the methods and outcomes of the persuasive acts, because it is not capable of forming its own intentions or making its own choices. Hence, it is not a free moral agent [START_REF] Berdichevsky | Toward an ethics of persuasive technology[END_REF].

Persuasion in argumentation has received a large amount of attention during the last years [START_REF] Bench-Capon | Persuasion in practical argument using value-based argumentation frameworks[END_REF][START_REF] Hunter | Empirical methods for modelling persuadees in dialogical argumentation[END_REF][START_REF] Hunter | Towards a framework for computational persuasion with applications in behaviour change[END_REF][START_REF] Hadoux | Strategic dialogical argumentation using multi-criteria decision making with application to epistemic and emotional aspects of arguments[END_REF][START_REF] Verheij | Grounded semantics as persuasion dialogue[END_REF][START_REF] Rosenfeld | Strategical argumentative agent for human persuasion[END_REF]. Also, ethics in argumentation has been studied with the focus on the use of normative systems [START_REF] Pigozzi | Arguing about constitutive and regulative norms[END_REF][START_REF] Bench-Capon | Norms and value based reasoning: justifying compliance and violation[END_REF][START_REF] Liao | Building jiminy cricket: An architecture for moral agreements among stakeholders[END_REF].

However, we can observe that the researchers and practitioners who develop argumentative persuasive chatbots are not always aware of the existing advances and the state of the art in the domain of persuasion as well as of the current legal framework and corresponding ethical considerations. This is why the first goal of our paper is to make a bridge between the existing knowledge in persuasion and ethics on one side and the practitioners who develop argumentative persuasive chatbots on the other side. This is why the first part of the paper is devoted to a survey of the literature on persuasion related to argumentative persuasive chatbots. We cover the state of the art of persuasion for persuasive chatbots and the corresponding ethical guidelines (Section 2). Then, we survey the existing legal framework and its link with ethical principles and critically analyze the new proposal for a regulation on artificial intelligence of the European Commission (Section 3). We believe that this overview will be useful for researchers in argumentation who want to deploy an argumentative chatbot for persuasion since it allows them to quickly get knowledge about the most relevant approaches at one place.

The second part of the paper is devoted to the question: how can we increase transparency, trust and effectiveness of the argumentative persuasive chatbots? We propose a method that can enhance explainability and transparency of the persuasion systems (Section 4). The main idea is to show the graphical representation of the arguments used during the persuasion to the user at the end of the dialogue. This representation contains the relations between the arguments (attacks, supports), their origin (source), who uttered them (i.e. the machine or the human participant) and the persuasive methods employed. This approach has several advantages. Namely, it makes the system more transparent and enhances the human understanding of the system, which is a benefit per se. Furthermore, the fact that the system is transparent increases the trust of the user, which (apart from being one of the goals of AI in general) can increase the chance that the user will be persuaded by the system. Finally, the user can give a feedback on the presented arguments (e.g. how much they believe the arguments are ethical), which constitutes valuable data, which can later be used to better understand the underpinnings of human reasoning and improve the persuasion system.

The paper is organised as follows. Section 2 presents related work in persuasion that is relevant for argumentative persuasive chatbots and corresponding ethical guidelines. Section 3 surveys the legal and ethical aspects. Section 4 explains the pillars of our idea to use the graphical argumentation-based representation in order to enhance transparency, trust and efficiency of persuasion systems. We then talk about possible future work and conclude.

State of the Art in Persuasion

This section provides background material for argumentative persuasive chatbots and the corresponding ethical guidelines.

Persuasive Strategies

In this subsection, we review some of the persuasive strategies that can be used to enhance persuasion. Fogg was able to identify and propose forty principles that persuasive technologies can use [START_REF] Fogg | Persuasive technology: using computers to change what we think and do[END_REF]. The forty principles are classified into six categories. The first three categories are the three persuasive roles computing techonlogies can play. Computers can behave as persuasive tools, persuasive media and persuasive social actors. The three other categories study how computers and web pages can be more persuasive through credibility, mobility and connectivity. Table 1 shows the different principles proposed by Fogg classified into the six categories. We discuss only the principles that seem most relevant for persuasive chatbots. We present briefly the principles related to the role of persuasive technologies as social actors. As Fogg explains, there exist five essential types of social cues that persuasive technologies can convey. First, physical cues can be transmitted through the physical characteristics. Fogg proposes the principle of Attractiveness: "A computing technology that is visually attractive to target users is likely to be more persuasive as well." Second, persuasive technologies can use psychological cues to persuade. Fogg defines a person's psychology as the group of emotions, preferences, motivations and personality. Humour and empathy can also be considered as psychological cues. For example, a chatbot possesses empathy when it acknowledges the user's feelings, shows compassion and supports the user. The expressions like "I understand your feeling" and "I am sorry to hear that" are signs of having empathy towards the user. The principle of Similarity states: "People are more readily persuaded by computing technology products that are similar to themselves in some way." Third, we have the language. The principle of Praise states that: "By offering praise, via words, images, symbols, or sounds, computing technology can lead users to be more open to persuasion." Fourth, social dynamics such as giving praise, cooperation and reciprocity can also be used. The principle of Reciprocity proposed by Fogg states that: "People will feel the need to reciprocate when computing technology has done a favour for them." Finally, adopting a social role can be considered as a very effective persuasive technique. Specifically, adopting an authority role seems to be very effective for persuasion. The principle of Authority identified by Fogg is the following: "Computing technology that assumes roles of authority will have enhanced powers of persuasion." We also present some of the principles related to "credibility and computers" which can be considered relevant for persuasive chatbots. Fogg explains that there are two element keys for credibility: trustworthiness and expertise. Trustworthiness represents how much truthful, fair and unbiased a source can be perceived. The principle of Trustworthiness states: "Computing technology that is viewed as trustworthy (truthful, fair and unbiased) will have increased powers of persuasion." Expertise represents how much perceived knowledge, skill and experience a source can have. The principle of Expertise states: "Computing technology that is viewed as incorporating expertise (knowledge, experience and competence) will have increased powers of persuasion." Fogg also points out to the fact that computing technology tends to lose credibility easily if commits a significant error. Once the credibility is lost, it may be hard to regain. Therefore, Fogg proposes the principle of (Near) Perfection that states: "Computing technology will be more persuasive if it never (or rarely) commits what users perceive as errors." Cialdini was able to identify six influence principles: Reciprocity, Commitment and Consistency, Social Proof, Liking, Authority, Scarcity [START_REF] Cialdini | Influence: The Psychology of Persuasion[END_REF]. Among these principles, we explain briefly the ones that were not explained before. The Commitment and Consistency principle states that humans tend to commit to their opinions, values and choices. The Social Proof principle states that people tend to imitate other people's ideas and actions. For example, if a person is considering buying a product online but they are not sure whether the product is good or convenient for them, they can check the product's reviews section. If most of the reviews are positive, the user tends to feel more confident in their decision in purchasing the product. The Liking principle is close to the Similarity principle proposed by Fogg, it states that people like others who are similar to them. The Scarcity principle states that people tend to value opportunities or things that become less available, hence scarce. Oinas-Kukkonen and Harjumaa proposed and developed a Persuasive Systems Design model [START_REF] Oinas-Kukkonen | Persuasive systems design: Key issues, process model, and system features[END_REF] where twentyeight persuasive strategies were listed for the design of persuasive technology. These principles were divided into four categories: primary task, dialogue, system credibility, and social support.

Wang et al. were able to identify ten persuasion strategies that are divided into two types, the persuasive appeal and the persuasive inquiry type. The persuasive appeal type consists of trying to appeal to the persuadee's psychology. The persuasive inquiry type consists of asking the persuadee personal questions to facilitate the persuasion [START_REF] Wang | Persuasion for good: Towards a personalized persuasive dialogue system for social good[END_REF]. The persuasive appeal strategies identified in this work are the following: The Logical appeal strategy consists of using evidence and reasons to convince the persuadee of the persuasion goal. The Emotional appeal strategy consists of evoking the persuadee's positive and/or negative emotions. The Credibility appeal strategy consists of citing information from objective sources in order to gain the persuadee's trust. The Foot-in-thedoor strategy consists of asking the persuadee small requests first, then asking larger ones. The Personal story strategy consists of telling the persuadee stories about other people who were persuaded by the persuasion goal, focusing on the positive results of such persuasion. The Donation Information strategy consists of giving the persuadee information about the action or idea the persuader wants to convince them with. The persuasive inquiry strategies are the following: The Source-related inquiry strategy consists of asking the persuadee whether they are aware or not of the action or idea the persuader wants to persuade them with. The task-related inquiry strategy consists of asking the persuadee their own opinions and expectation concerning the persuasion goal. Finally the personal-inquiry strategy consists of asking the persuadee about their own personal experiences related to this persuasion goal. .

Personalization in Persuasion

In this subsection, we briefly define personalized persuasion and we briefly review two works done in this field. Personalization plays an important role in enhancing persuasion. Personalized persuasion consists of using the user's personal information and background to enhance the outcome of persuasion [START_REF] Kaptein | Personalizing persuasive technologies: Explicit and implicit personalization using persuasion profiles[END_REF][START_REF] Orji | Personalization in persuasive technology[END_REF][START_REF] Ciocarlan | Actual persuasiveness: impact of personality, age and gender on message type susceptibility[END_REF][START_REF] Liao | How should AI systems talk to users when collecting their personal information? effects of role framing and self-referencing on human-AI interaction[END_REF]. Apart from using the user's psychological cues to persuade them, personalization can appear in the form of trying to adapt the methods used during the process of persuasion based on the user's psychological profile and/ or personal information. The goal of such adaptation is also the enhancement of the persuasion outcome. Kaptein et al. studied the effects of involving users in choosing a specific influence strategy for persuasion, disclosing the usage of such strategies and the use of multiple strategies simultaneously on user compliance to persuasive attempts [START_REF] Kaptein | Means based adaptive persuasive systems[END_REF]. The authors consider these results as guidelines for designing adaptive persuasion systems. Adaptive persuasion systems are persuasion systems that use different influence strategies based on the users' profiles in order to increase their influence on users. The authors proved that letting the user decide which strategy to adopt in order to persuade them is more effective than predicting the preference for a specific strategy based on behaviour or personality measures. The reason behind this is that if the user chooses which influence strategy to adopt, they will commit to choice that they have made. The authors have also proved that using a single preferred strategy is more effective than using the preferred strategy simultaneously with a non preferred one. Also, using the two strategies simultaneously was more effective than using the single non preferred strategy alone. The two persuasive strategies used in this work are the authority and the consensus strategies. Wang et al. studied how the variations of the persuasion strategies upon the user's psychological background, affect the persuasion outcome [START_REF] Wang | Persuasion for good: Towards a personalized persuasive dialogue system for social good[END_REF]. The results of the work done by Wang et al. [START_REF] Wang | Persuasion for good: Towards a personalized persuasive dialogue system for social good[END_REF] and Shi et al. [START_REF] Shi | Effects of persuasive dialogues: testing bot identities and inquiry strategies[END_REF] show that personalizing the persuasion strategies yields better persuasion outcomes.

Persuasive Chatbots

In this subsection, we briefly review four persuasive chatbots that were developed and designed. The first three chatbots use the Credibility Appeal (Trustworthiness and Expertise) strategy because all the arguments presented by the chatbots come from objective sources (scientific sources, governmental websites and experts) therefore the information presented by the chatbots are unbiased, fair and truthful. As for the fourth chatbot, it uses different persuasive strategies during the dialogue. Altay et al. studied whether they can change people's opposite opinion regarding genetically modified food and genetically modified organisms by providing rebuttals to the counterarguments held against genetically modified organisms [START_REF] Altay | Scaling up interactive argumentation by providing counterarguments with a chatbot[END_REF]. They have defined four conditions: the Control Condition consists of defining genetically modified organisms. The Consensus Condition informs about the important existence of the scientific consensus regarding the genetically modified organisms' benefits. The Counterarguments Condition presents first counterarguments against genetically modified organisms then rebuttals to these counterarguments, then rebuttals to the previous ones and so on. The user scrolls to check all the arguments which were presented in a clear dialogue structure. Finally, the Chatbot Condition consists of presenting all the counterarguments against genetically modified organisms to the user, the user can click on any counterargument, the rebuttal of the counterargument selected appears progressively. The user has the option to come back to the initial counterarguments and click on another one. The four conditions were compared against each other. The Counterarguments Condition was found more persuasive than the Chatbot condition in the sense that spending more time reading when all counterarguments are available leads to more positive attitude changes than selecting only the most relevant counterarguments.

Hadoux and Hunter showed how preferences over types of concern can be used to enhance the persuasion in persuasive dialogues [START_REF] Hadoux | Comfort or safety? gathering and using the concerns of a participant for better persuasion[END_REF].The notion of concern is defined as an issue raised or addressed by an argument. Among the empirical studies conducted, a group of participants chatted with two types of chatbots called the baseline chatbot and the preference-based chatbot. The structure of the dialogue is the same for both chatbots, the first argument is the one proposed by the system and called the persuasion goal, then a menu of counterarguments is proposed by the system to the user. The user can select a set of counterarguments; this set is called a menu move. Then the system can counter the selected arguments by a set of arguments called the posit move. The dialogue continues in the same manner until there is no argument attacking any argument of the last move or if the user ends the dialogue by a null argument. A null argument means that the user does not choose any of the counterarguments presented by the system. For the baseline chatbot, for each counterargument selected by the user, the chatbot selects a rebuttal among the arguments that attack the counterargument randomly. For the preference-based system, a set of the user's preferences over concerns is available, for each counterargument selected by the user, the chatbot selects the rebuttal with which the most preferred type of concern is associated. The results show that using preferences over concerns enhances the persuasiveness of the dialogue.

Chalaguine and Hunter designed a persuasive chatbot to persuade users to take the Covid-19 vaccine [START_REF] Chalaguine | Addressing popular concerns regarding covid-19 vaccination with natural language argumentation dialogues[END_REF]. The authors use the same notion of concern as in [START_REF] Hadoux | Comfort or safety? gathering and using the concerns of a participant for better persuasion[END_REF]. The chatbot first presents the persuasion goal, then the user provides a counterargument manually. The chatbot predicts the user's concern raised in the counterargument and replies with the first not yet used rebuttal that addresses the same concern. In case the chatbot could not identify the concern of the user i.e. the prediction is less than 40% in confidence, it replies with one among three default rebuttals. There exist only three default rebuttals. The dialogue ends when the chatbot cannot identify a concern and all of the three default rebuttals were already used. The authors have shown that this interactive chatbot is more persuasive than a static web page in which the users read the ten most common rebuttals used by the chatbot. Shi et al. developed a persuasive chatbot that understands the user's input based on neural network models [START_REF] Shi | Effects of persuasive dialogues: testing bot identities and inquiry strategies[END_REF] and replies from the human responses that were collected from the previous work [START_REF] Wang | Persuasion for good: Towards a personalized persuasive dialogue system for social good[END_REF]. The authors conducted experiments to study the effect of disclosing the chatbot's identity (bot or human) and the effects of using different type of inquiries (personal and/or non personal inquiries) on the persuasiveness of the dialogue. The chatbot salutes the user first, then the chatbot asks the user some questions based on the type of persuasive inquiry the user was assigned to. After finishing from the persuasive inquiry module, the chatbot moves on to the persuasive appeal module where the chatbot dialogues with the user. At each step, the chatbot uses a different persuasive strategy and asks the user if they want to donate. If the user accepts to donate, that means that the chatbot succeeded in persuading the user to donate. If the user does not accept to donate, the chatbot uses another not yet used persuasive strategy to try to persuade the user to donate. The dialogue ends if the user agrees to donate or if the ten persuasive strategies were all used. Results showed that whether the chatbot was really human or not, it is the perceived identity of the chatbot by the user that matters. The persuasiveness is better when people think they are talking to human.

Argumentation Theory in Persuasive Dialogues

In this subsection, we review the main elements of a persuasive dialogue system introduced by Prakken [START_REF] Prakken | Formal systems for persuasion dialogue[END_REF]. Argumentation-based dialogues can be classified into six types based on their goal [START_REF] Baroni | Handbook of Formal Argumentation[END_REF]. We have persuasion, negotiation, information seeking, deliberation, inquiry and quarrel. When it comes to argumentationbased dialogues, there are multiple rules to take into account. The communication language consists of the utterances the participants can make, the protocol consists of the conditions under which the participants can make the utterances, it also determines when the dialogue ends. According to Prakken [START_REF] Prakken | Formal systems for persuasion dialogue[END_REF], the main elements of a persuasive dialogue systems are the following:

-A dialogue goal which, in persuasion dialogues, is the resolution of a conflict of point of views between the participants. -A topic language -A logic for the topic language used, which can be monotonic or non-monotonic, it can be used to manage the dialogical consistency of participants. -A communication language: As defined before, communication language consists of the allowed utterances to make. The most important ones are : claim ϕ, why ϕ, concede ϕ, retract ϕ, question ϕ and ϕ since S. As explained by Prakken [START_REF] Prakken | Formal systems for persuasion dialogue[END_REF], Claim ϕ means that the speaker asserts that ϕ is the case. Why ϕ means that the speaker challenges that ϕ is the case and asks for reasons why it would be the case. Concede ϕ means that the speaker admits that ϕ is the case. Retract ϕ means that the speaker declares that they are not committed (any more) to ϕ. Question ϕ means that the speaker asks another participant's opinion on whether ϕ is the case. Finally, ϕ since S means that the speaker provides reasons why ϕ is the case. -A protocol: The protocol specifies the allowed moves at each step of the dialogue. The protocol specifies the dialogue's structure. We have uniquereply vs multi-reply, unique-move vs multi-move and immediate-reply vs non-immediate-reply, deterministic and fully deterministic vs non deterministic protocols. -A set of participants with roles, internal beliefs and commitments. Usually the roles in a persuasion dialogue are proponent, opponent and neural toward a well specified topic. Commitments are very important because they determine the end of the dialogue and its outcomes, and they can be used to oblige the participant to be dialogically consistent. Commitments are usually determined by claim ϕ , concede ϕ and retract ϕ. -Effect rules: The effect rules determine the effects of the speech acts on the participants' commitments. -Outcome rules: The outcome rules define the outcomes of a dialogue which are in a persuasive dialogue the winners and the losers of the dialogue.

Ethics of Persuasive Chatbots

In this subsection, we cover the ethical guidelines proposed in the literature for the design of persuasive technologies and we try to orient some of them towards the design of persuasive chatbots precisely. Berdichevsky and Neuenschwander explain that when it comes to persuasion, both persuader and persuadee take full moral responsibility for the outcome [START_REF] Berdichevsky | Toward an ethics of persuasive technology[END_REF]. In order to evaluate the ethics of persuasion itself, one should evaluate the persuader's motivations, the methods they employed and the outcome of persuasion.

Although persuasive technology and persuasive people have same motivations and use similar methods and strategies, persuasive technology has more persuasive potential because of the simulations they can embed leading to more realism. The difference between persuasion through technology and through person-toperson interactions relies in the methods used for persuasion and also probably the outcome. The ethics of persuasion seems to be insufficient to guide the design and implementation of persuasive technology. The authors wanted to reconsider the ethical guidelines for traditional persuasion methods when being applied by technology and not humans, and for the outcome i.e. the persuasion goal.

Therefore, Berdichevsky and Neuenschwander proposed a set of eight ethical principles and guidelines for the design and implementation of persuasive technology, with the consideration that the designers should be only responsible for intended and unintended reasonably predictable outcomes [START_REF] Berdichevsky | Toward an ethics of persuasive technology[END_REF]. The first two principles state that the intended outcome of any persuasive technology and the motivations behind it should never be considered unethical if the persuasion was done without the technology or if the outcome happened independently of the persuasion. The third principle states that the designers of such technologies should take responsibility for all reasonably predictable outcomes of their use. The "Dual Privacy" principles state that the creators of persuasive technology should respect users' privacy when it comes to accessing their online personal information and sharing it with a third party. The "Disclosure" principle states that the designers must be transparent and clear about the motivations, methods and intended outcomes of such technology. The "Accuracy" principle states that the persuasive technology should always be honest and credible. Finally, the "Golden" principle states that the designers of persuasive technology should never use a persuasion goal that they themselves are not consent of being persuaded by it.

Verbeek [START_REF] Verbeek | Persuasive technology and moral responsibility toward an ethical framework for persuasive technologies[END_REF] emphasised on the importance of integrating the ethical framework proposed by Berdichevsky and Neuenschwander [START_REF] Berdichevsky | Toward an ethics of persuasive technology[END_REF] with the concept of "technological mediation" in order to better understand and predict unintended outcomes, and take these outcomes into consideration when designing persuasive technology. As mentioned before, technology tends to shape human perception and interpretation of reality by amplifying some perceptions while reducing others. It also shapes human actions in reality by encouraging specific forms of actions while discouraging others. Persuasive technology mediates these effects between users and their environments, so when technology is used the way their designers intended, it is possible to have unintended and unexpected outcomes, Verbeek proposed to focus on all the mediation effects by doing a moral reflection along deontological and utilitarian lines. The deontological point of view means respecting the moral principles while the utilitarian point of view means balancing between the desirability for something and its costs for all the people involved. This moral reflection will take into consideration the intended persuasions, the methods of persuasion used with the emerging forms of mediation, and the outcomes of the mediation which are the consequences of the persuasive and mediating role of the technology. The ethical guidelines that the authors proposed are the following:

-The intended persuasions of the technology-in-design must cause no harm for the people using persuasive technologies and those affected by them being used, these intended persuasions must benefit these people and be fair (justice) to them. -The methods of persuasion and forms of mediation must be disclosed (respect for autonomy), cause no harm in terms of privacy and be fair to all people. -As for the outcomes of mediation, the designers must do a moral imagination of all the possible mediating roles of technology in human actions and experiences and then assess these mediations along the deontological and utilitarian lines.

Fogg proposed to apply a stakeholder analysis to identify all people affected by a persuasive technology, and what each stakeholder has to gain or lose [START_REF] Fogg | Persuasive technology: using computers to change what we think and do[END_REF].

-List all of the stakeholders.

-List what each stakeholder has to gain.

-List what each stakeholder has to lose.

-Evaluate which stakeholder has the most to gain.

-Evaluate which stakeholder has the most to lose.

-Determine ethics by examining gains and losses in terms of values.

-Acknowledge the values and assumptions you bring to your analysis.

Note that values differ from a culture to another. Hence, creators of persuasive technology must be careful about the culture in which they are embedding this technology, because with every different culture, comes different ethical issues.

We list below a set of guidelines for the design of persuasive chatbots inspired by the guidelines proposed by Fogg [START_REF] Fogg | Persuasive technology: using computers to change what we think and do[END_REF] for the design of persuasive technology.

-Users of persuasive chatbots should not be distracted by the number of questions or the difficulty of arguments, because this can stand in the way of their focus on the content in the chatbot. The chat must not be complicated or very lengthy.

-Creators of persuasive chatbots should not consider that the user has experience in the domain of the goal with which we want to persuade the user. -Creators of persuasive chatbots should not include in the chat any links to download an application or something else. -The user must be able to stop at anytime they want, or ask for clarification.

The creators should be also careful about the cases where the user must have the ability to ask for a human intervention. -Not only the creators of persuasive chatbots take responsibility when it comes to errors and damage to the user, but also the company that bought this technology, distributed and promoted it. We may have different companies through time, they all can be responsible. -Manipulation can happen when the chatbot expresses negative or positive emotions towards the user, presents arguments that appeal to the user's positive/negative emotions, tells lies or false information, tells incomplete information, chats with children or mentally disabled people, presents threatening information or punishment. Negative emotions could be fear, angry, deception, impatience. Positive emotions could be celebration, rewarding, encouraging. Designers of persuasive chatbots should avoid manipulation at all costs. It is preferred that the chatbot does not express emotions at all or expresses the minimum and only for good cause. -The chatbot should not be very sophisticated in a way that confuses the user whether the chatbot is a human or robot. The user must know that they are chatting with a robot. -Designers of persuasive chatbots must test and supervise these chatbots when used to observe if there are any unintended outcomes that were not recognised before, or happen to a small number of people. This is how they should deal with unintended and unpredictable outcomes. They should also keep track of the conversations between the chatbot and the users, with the user's knowledge. -Persuasive chatbots should not provide offers, promotions, advertisements or branding. They should be designed exclusively for persuasion.

Creators of persuasive chatbots are also invited to consult the guidelines for developers of conversational AI proposed by Microsoft [START_REF] Corporation | Responsible bots: 10 guidelines for developers of conversational AI[END_REF].

Persuasive Chatbots between Ethics and Law

In this section, we present and discuss the legal issues that impact the design and implementation of chatbots, specifically persuasive chatbots. On April 21, 2021 the European Commission has published a proposal for a regulation on artificial intelligence [START_REF] Ebers | The European commission's Proposal for an artificial intelligence act-a critical assessment by members of the robotics and AI law society (rails)[END_REF], called the AI Act, which is currently under discussion and will soon be adopted [START_REF]Council laying down Harmonised Rules on Artificial Intelligence and amending certain Union Legislative Acts (AI Act)[END_REF]. The AI Act proposes a gradation of legal constraints according to the risks presented by the AI system. These risks are those relating to health, safety, fundamental rights and environment. AI systems are therefore classified into the following categories:

-"Unacceptable Risk" (social scoring, subliminal techniques, biometric categorisations, "real-time" remote biometric identification systems in publicly accessible spaces, etc.), the use of which is banned, sometimes with some exceptions.

-"High Risk" (Annex III cites biometrics, management of critical infrastructure, educational and vocational training, employment, workers management and access to self-employment tools, access to essential public and private services, etc.), the use of which must follow strict obligations and requirements so that the AI system can be placed on the market in the European Union.

-"Low (AI systems intended to interact with people (i.e. Chatbots), deep fakes, emotion recognition systems, etc.), the use of which requires compliance with an obligation of transparency (Article 52).

-"Minimal Risk" (e.g. Video games and spam filters based on AI), for which the AI Act does not impose any specific obligation.

The AI Act is one of the first texts in the world that will impose legal obligations for chatbots, when the text currently under discussion is voted on. The 2021 Proposal contained very few obligations regarding chatbots. Indeed, users brought to interact with a chatbot only needed to know that they were discussing with a machine in order to be able to choose whether to continue the discussion or not. However, the latest versions are much more precise [START_REF]Council laying down Harmonised Rules on Artificial Intelligence and amending certain Union Legislative Acts (AI Act)[END_REF]. On the one hand, the amendments to the Proposal explain how to provide the information. Article 52.1 now states that: "Providers shall ensure that AI systems intended to interact with natural persons are designed and developed in such a way that the AI system, the provider itself or the user informs the natural person exposed to an AI system that they are interacting with an AI system in a timely, clear and intelligible manner, unless this is obvious from the circumstances and the context of use." The information must therefore be provided either by the provider itself or by the chatbot, or by the professional user. This information must also be provided in a way that is clear, intelligible and at the most late at the time of the first interaction (Article 52.3, b) so that the person can choose not to use it, unless the fact that they are interacting with a chatbot is obvious to the taking into account the circumstances and the context of use. In addition, this text now also requires that the provider indicates which functions are AI enabled, if there is human oversight, and who is responsible for the decision-making process, as well as the possibilities to object against the application and to seek judicial redress against decisions taken by or harm caused by AI systems. However, Article 52.1 relates to chatbots in general, but not specifically to persuasive chatbots. Therefore, this information obligation may not be sufficient to protect users in this context.

In order to guarantee more complete transparency for the users of a persuasive chatbot, it seems important that the providers also inform them of the persuasion strategy underlying their system. For example, it is relevant that the users of an authority-based persuasion chatbot are informed of the designers' goals. The AI Act does not directly address this concern. This therefore means that if this text remains as it is on this subject, people interacting with a persuasive chatbot could be incompletely protected. The ethical approach is a response to this concern, because it makes it possible to reinforce the law in order to do what is well beyond what is only legal. European policies relating to the ethics of AI also adopt this vision [START_REF] Nevejans | What place for ai ethics in consumer protection in the light of the ai act and beyond?[END_REF].

We therefore argue that simply informing the users that they are interacting with a chatbot is insufficient for a persuasive chatbot. It is therefore crucial to provide them with additional ethical information:

-Users must be informed about the nature of the persuasion system used.

-Users should be made aware of the potential effects of the persuasive system used, particularly if the persuasive effect could be enhanced.

Can the designers of a persuasive chatbot be likely to infringe the rights and freedoms of users? We believe that if the chatbot adapts its method of persuasion according to the gender, racial origins, or religious beliefs of users, it might risk to behave in a discriminatory way. While the previous versions of the AI Act neglected these risks for rights and freedoms, the latest amendments reveal the desire to integrate the ethical principles of AI which had only been mentioned in the Guidelines or other non-binding texts of the European Union [START_REF] Nevejans | What place for ai ethics in consumer protection in the light of the ai act and beyond?[END_REF]. Thus, the new Article 4, a) concerns the "General principles applicable to all AI systems" which must be respected by all operators, including the provider and the professional user (i.e. deployer for the latest versions of the AI Act), whose AI system falls within the scope of the AI Act. This is indeed the case of providers of persuasive chatbots which, as low-risk AI systems, must respect six new fundamental principles which are:

-Human agency and oversight -Technical robustness and safety -Privacy and data governance -Transparency -Diversity, non-discrimination and fairness -Social and environmental well-being If these principles end up being definitively voted on, we can therefore think that a chatbot will have to respect the principles of non-discrimination and equity. However, it should be borne in mind that the goals of the designers of the persuasive chatbot may only be discriminatory in appearance. Indeed, it is quite conceivable that designers adapt their method of persuasion, for example, to the age or level of education of the user, which would then be a simple way for designers to be better understood or for the chatbot to be more easily used by specific users.

If the chatbot is gendered or humanised, it is still likely to infringe the rights and freedoms of users in two cases. In the first case, certain human aspects can reinforce the persuasive effect. For example, the chatbot can appear in the form of a gentle face of a grandmother who softens the users. Designers must be aware of this and minimise these characteristics. In the second case, gender or certain human aspects can be potentially sexist or discriminatory depending on the uses that are made of them and the goals that designers pursue. One of the solutions could be to minimise the human characters to avoid the problems of sexist or discriminatory biases. However, it is possible for a non-gendered persuasive chatbot without human characteristics to be less persuasive for the purposes pursued by the designer. In this case, a compromise must be made between the values to be respected and the goals to be achieved by the chatbot.

Designers of a persuasive chatbot can still infringe people's rights and freedoms if the chatbot is designed to manipulate users, for example by leading them in a certain direction without their knowledge or saying things that are false or truncated. The latest version of the AI Act, which now prohibits the use of deliberately manipulative or deceptive techniques in Article 5.1, a) as an unacceptable risk AI system, seems to come closer to the objectives pursued by a persuasive chatbot. In reality, the prohibition concerns cases where the manipulative technique would seriously harm the person: "the placing on the market, putting into service or use of an AI system that deploys subliminal techniques beyond a person's consciousness or purposefully manipulative or deceptive techniques, with the objective to or the effect of materially distorting a person's or a group of persons' behaviour by appreciably impairing the person's ability to make an informed decision, thereby causing the person to take a decision that that person would not have otherwise taken in a manner that causes or is likely to cause that person, another person or group of persons significant harm." Therefore, the use of a persuasive chatbot, except for the purpose of achieving the extreme results referred to in Article 5.1, a), is not prohibited under the current state of the AI Act. The question is nonetheless also very delicate from an ethical point of view, since the motivations of designers can vary considerably. However, we believe that the majority of the designers have good intentions and want to use the chatbot for users' own good. As an example, take the the chatbots that try to persuade users to practice a sporting activity or to limit the consumption of alcohol or sugar.

Ethical Design via Explainability

There is an increasing interest in Explainable AI over the last few years in order to tackle the ethical challenges that arise from the use of AI-based technologies. Vilone and Longo list the existing definitions in the literature of the notions related to the concept of explainability [START_REF] Vilone | Notions of explainability and evaluation approaches for explainable artificial intelligence[END_REF]. We believe that we can respect the ethical guidelines for persuasive chatbots [START_REF] Berdichevsky | Toward an ethics of persuasive technology[END_REF] by using argumentation for the explainability of persuasive chatbots. In this work, we use mostly two notions of explainability: understainability and correctability. Understainability means the capacity of a method for explainability to make a model understandable while correctability means the capacity of a method for explainability to allow end-users make technical adjustments to an underlying model [START_REF] Vilone | Notions of explainability and evaluation approaches for explainable artificial intelligence[END_REF]. Our method consists of showing an argumentation graph to the user after the dialogue: that graph highlights the persuasive methods and the sources of information used by the chatbot, and the degrees to which the user finds the chatbot's arguments ethically acceptable. Before explaining our method, we briefly present Dung's abstract argumentation framework.

Dung's Abstract Argumentation Framework

In abstract argumentation [START_REF] Dung | On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games[END_REF][START_REF] Besnard | Elements of Argumentation[END_REF][START_REF] Amgoud | Evaluation of argument strength in attack graphs: Foundations and semantics[END_REF], arguments are considered defeasible entities where all information related to these arguments are abstracted away except for the relations of attacks between them. Dung's argumentation framework [START_REF] Dung | On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games[END_REF] is one of the attempts used to formalise reasoning i.e. to represent systems of arguments and their relations, determine which arguments are acceptable.

An Argumentation-Based Approach Towards Explainability

Our method consists of labelling each argument presented by the chatbot by a persuasion strategy (if it exists) and by the source of the information presented in the argument. If the chatbot uses natural language processing to generate the arguments presented to the user, this process is called post-labelling because the chatbot labels the arguments after they were presented to the user. In the other case where the chatbot has already a knowledge base i.e. well defined arguments in its system, then each argument must be pre-labelled. For both cases, the user chats with the persuasive chatbot.

When the dialogue ends, the chatbot shows an argumentation graph that consists of all the arguments that were presented during the dialogue by both sides, with the relations (attacks) between them. By showing this argumentation graph, the chatbot shows the persuasion strategies that were used during the process of persuasion to the user. Hence, the chatbot discloses all the methods employed in the dialogue. It also shows the source of the information it provided in each argument. The information can be extracted from scientific sources, crowd-sourcing, online forums, governmental websites, personal communication with experts, etc. Also, it can be generated by the chatbot i.e. invented.

Adopting this method allows the user to assess the accuracy of the information that was given by the chatbot and to possibly detect if the chatbot lied or stated false information. By implementing this method, we answer to the question : How did the chatbot try to persuade the user?

We also allow the user to input for each argument presented by the chatbot, a degree that ranges between 0 and 1. Each degree associated with a specified argument must represent the user's belief in the argument being (somehow) ethical. We let this assignment be spontaneous and intuitive in order to be able to represent the user's actual beliefs and preferences over what is considered ethical and what is not. Assigning a degree to each argument presented by the chatbot will let us know the set of preferences of the user over the persuasive strategies and over the sources of information provided. Hence, we can build a recommendation system which predicts the set of preferences of a user based on their personal information and/or personality measures. This way, the chatbot can be considered ethically adaptive. Also, this can help the designers of the chatbot to eliminate from the chatbot's knowledge base the persuasive strategies or even the arguments that were assigned a very low degree of being ethical. Figure 2 and Figure 3 show examples of abstract argumentation graphs presented to the user after the dialogue with the chatbot.

Conclusion

In this paper, we studied ethical argumentative persuasive chatbots. In the first part of the paper, we reviewed the state of the art of persuasion for argumentative persuasive chatbots and the ethical guidelines for the design of such systems. First, we provided background material for the persuasive strategies that can be used by persuasive chatbots to enhance the persuasion. Then we discussed how personalization used in persuasion can be helpful to improve the persuasive effect. We also reviewed four argumentative persuasive chatbots where we focused on the dialogue structure, and we briefly studied argumentation in persuasive dialogues. Finally, we presented the state of the art of ethics in persuasive technologies and we made a list of the ethical guidelines that designers of persuasive chatbots are invited to respect. We also discussed the legal constraints related to design and implementation of persuasive chatbots and we showed how ethics could complement the legal framework in order to better respect the user's freedoms and rights.

In the second part of the paper, we proposed to use argumentation to display the persuasive strategies employed by the chatbot and the source of the information presented by the chatbot to the user. This way, the chatbot discloses the persuasive methods it used and provides to the user more transparency by providing for them the source of the information presented in the arguments. We also proposed to assess how much ethical each argument presented by the chatbot is, by letting the user input how much they believe each argument is considered ethical. This way, to eliminate the arguments that have very low degrees in the next dialogue, and we can ethically adapt the arguments presented by the chatbot to the user's preferences.

Definition 1 .

 1 An abstract argumentation framework AF ) is a pair ⟨Ar, att⟩ where Ar is a finite and non-empty set of arguments and att ⊆ Ar × Ar is an attack relation (→).

Figure 1 Fig. 1 .

 11 Figure 1 shows an example of an abstract argumentation framework with arguments represented by nodes, and relations of attacks among them.

Fig. 2 .

 2 Fig. 2. Example of an abstract argumentation graph representing the dialogue between the user and the chatbot."PG" stands for persuasion goal. The nodes in blue represent the chatbot's arguments while the nodes in pink represent the user's arguments. The first row of labels represents the persuasive strategies used by the chatbot. The second row represents the source of the information presented by the chatbot. The third row represents the degrees assigned by the user.

Fig. 3 .

 3 Fig. 3. Example of an abstract argumentation graph representing the dialogue between the user and the chatbot. The chatbot tries to persuade the user to do meditation. The nodes in blue represent the chatbot's arguments while the nodes in pink represent the user's arguments.

Table 1 .

 1 The forty persuasion principles proposed by Fogg for persuasive technologies.

	Computers as Persuasive Tools	Computers as Persuasive Media	Computers as Persuasive Social Actors
	Reduction	Cause and Effect	Attractiveness
	Tunnelling	Virtual Rehearsal	Similarity
	Tailoring	Virtual Rewards	Praise
	Suggestion	Simulations in Real-World Context	Reciprocity
	Self-Monitoring		Authority
	Surveillance
	Conditioning
	Credibility and Computers Credibility and the World Wide Web	Mobility and Connectivity
	Trustworthiness	"Real World Feel"	Kairos
	Expertise	Easy Verifiability	Convenience
	Presumed Credibility	Fulfilment	Mobile Simplicity
	Surface Credibility	Easy-of-Use	Mobile Loyalty
	Reputed Credibility	Personalization	Mobile Marriage
	Earned Credibility	Responsiveness	Information Quality
	(Near) Perfection		Social Facilitation
			Social Comparison
			Normative Influence
			Social Learning
			Competition
			Cooperation
			Recognition

  ://www.nccih.nih.gov/health/meditation-and-mindfulness-what-you-need-to-know 've heard that some people have negative experiences with meditation, like feeling anxious or restless. I am afraid that it might also happen with me. I understand how you feel. While that can happen in some cases, those experiences are often a sign that you're actually making progress in your practice.Meditation is a simple and cost-effective way to improve your overall health and well-being. You don't need any special equipment or a lot of time to get started, and the benefits are well worth the effort.

	Hi there! Have you ever thought about trying meditation?
	It has so many benefits for both your physical and mental health.
	I'm the CEO of a start-up, I'm too busy to do meditation	I can't sit still for that long
	Emotional Appeal		No Strategies Identified
	Source Not Specified		Source Not Specified
	0.6		0.7
	I understand that you may feel like	And as for sitting still,
	you don't have enough time,		there are actually many
	but even just a few minutes of meditation	different types of meditation
	each day can make a big difference	that don't require you to sit still
	in your overall well-being.		for long periods of time.
		Okay, but even if I did try it,
	Credibility and Authority	I don't think I would see any benefits.
	Source Specified
	0.9		Similarity and Emotional Appeal
	Well, actually, many health experts		Source Not Specified
	recommend meditation as a way to		0.6
	reduce stress, improve focus,		It's also worth considering that
	better sleep, and even lower blood pressure.		many successful and accomplished people,
			from athletes to CEOs, swear by meditation
			as a key part of their daily routine.
	Credibility and Consensus
	Source Specified
		0.8
	Also, there is scientific evidence that
	people who meditate regularly develop
	a greater sense of self-awareness and acceptance.
	There are many guided meditations available that can
	help you focus on positive thoughts and emotions.
	https://www.anahana.com/en/meditation/self-awareness
			What if I don't have the time or resources
			to try all those things?
	0.6 Source Not Specified Emotional Appeal	0.8 Source Not Specified No Strategies Identified

httpsI

Acknowledgements

This work benefited from the support of the AI Chair project Responsible AI (ANR-19-CHIA-0008) 4 and the project AGGREEY (ANR-22-CE23-0005), both from the French National Research Agency (ANR).


Amandine Decker
email: amandine.decker@univ-lorraine.fr

Maxime Amblard
email: maxime.amblard@univ-lorraine.fr

Ellen Breitholtz
email: ellen.breitholtz@ling.gu.se

Analysing topic shifts in task-oriented dialogues

Keywords: Modélisation de dialogue, changements de sujet, sémantique Topic modelling, Topic shifts, Semantics

Analyser les changements de sujet dans les conversations ayant un but.

Le sujet d'une conversation joue un role crucial dans sa cohérence globale car ce qui est discuté à un moment donné limite les contributions possibles des participants. Lancer un nouveau sujet de conversation alors que le précédent est encore en cours de discussion peut être une source de confusion en l'absence de signaux appropriés. Cependant, la notion de sujet de conversation est débattue en linguistique et n'est pas suffisamment abordée en modélisation du dialogue. Une description précise de cette notion ainsi que de celle de changement de sujet permettrait de comprendre ce que nous percevons lorsque nous jugeons comme cohérente une séquence d'interventions. Afin d'analyser les différents types de changements de sujet, nous proposons de créer un corpus de conversations ayant un but (discussion du dilemme éthique de la montgolfière), où les conversations sont des échanges de messages écrits. Ces discussions limitées thématiquement vont nous permettre de comprendre les changements de sujet dans les dialogues.

Motivation and context

Dialogue is at the core of human interactions, but it is also the place for many misunderstandings. It is thus interesting to build models to represent dialogue and try to better understand the features of a coherent interaction.

Interaction is however complex to characterise and model. In conversations, the participants exchange on different topics and the understanding of the current state of the dialogue by each participant relies on their interpretation of the topic under discussion. Even though topics are usually quite stable, shifts can happen more or less abruptly for several reasons such as a more pressing matter to discuss or the current topic being exhausted. Identifying topic shifts is crucial to understand and take part in the conversation. Conversely, creating confusion by changing the topic untimely and/or in an unclear manner could be seen as uncooperative [START_REF] Grice | Logic and conversation[END_REF]. Moreover, the topic can sometimes gradually change throughout the conversation which can make it complicated to keep track of the most relevant information.

Having a better understanding of topic shifts in dialogue would help us include topics and topic shifts in current dialogue modelling theories [START_REF] Amblard | Une analyse basée sur la S-DRT pour la modélisation de dialogues pathologiques[END_REF][START_REF] Hunter | Defining the Right Frontier in Multi-Party Dialogue[END_REF][START_REF] Maraev | Towards kos/ttr-based proof-theoretic dialogue management[END_REF][START_REF] Breitholtz | Enthymemes and Topoi in Dialogue: The Use of Common Sense Reasoning in Conversation[END_REF]. It could also enable us to analyse different types of dialogue-like conversations such as message threads on social media and identify off-topic contributions. Moreover, a symbolic approach of dialogue modelling could participate in improving language models' handling of abrupt or unconventional topic shifts. Topic shifts mechanisms are not all straightforward to investigate as participants usually implicitly agree on the topic and negotiate its boundaries through interaction. Thus, a more controlled setting with a fixed main topic, and more easily identifiable subtopics, could be very helpful to understand how people change the topic and react to topic shifts in dialogues.

Topic shifts mostly occur for three reasons: the dialogue content gives an idea to a participant (e.g., remembering an anecdote related to the current topic), the context changes and/or provides something to speak about (e.g., someone is coming who should not hear the current conversation, noises interrupt the dialogue, ...), or a participant has a specific agenda and shifts the topic, potentially abruptly, to fulfil it. In all these cases, the other participants must understand that the topic is changing to prevent disruptions in the dialogue. Specific tasks given to the participants could help us gather different types of topic shifts in our corpus.

Our objective is to construct a corpus that encompasses standard interactions, disrupted interactions, and interactions with multiple goals. The dialogues would be collected with a Chat Tool [START_REF] Healey | A dialogue experimentation toolkit[END_REF] that enables the experimenter to intervene in the conversations by modifying or delaying the participants' messages for example. This will facilitate the comparison of how participants handle topic shifts. Our goal is to gather data first in English and later in French and possibly Swedish. Based on these analyses, we will define a proximity measure for determining topic change. Using it, we could segment the dialogues into thematic units and propose a more general theory of interaction.

DiET (Dialogue Experiment Toolkit) is an experiment platform developed by [START_REF] Healey | A dialogue experimentation toolkit[END_REF] that provides ways to gather text-messages conversations in a controlled environment. This ChatTool allows experimenter to link participants either with the Telegram app or with a custom computer interface and to store their conversation. All the messages sent by the participants are passed to a server which relays the messages to the addressees. The transition through the server enables the experimenter to control the conversation for they have the possibility to interfere with the conversations. Different manipulations are possible, and both the Telegram app and the custom computer interface have specificities when it comes to the display functionalities.

The experimenter can modify the content of messages, the identity of the sender of the messages and the timing of the messages. They can also insert fake messages and make it look like one of the participants sent them. These manipulations, illustrated by Figure 1, are meant to make it possible to investigate different dialogue phenomenon such as the organisation of a conversation, miscommunication or dialogue coherence.

Phenomena to Observe

Our goal is to investigate speakers' understanding of topic shifts. In this context, we would like to analyse the following phenomena. Topic shifts are not always completely clear, especially when the new topic is not obviously different from the previous one. On the other hand, introducing a new topic that is completely different from the one at hand can be confusing and/or considered rude by the other participants. We would like to see how people react to more or less striking topic shifts.

There exist different ways to mark a topic shift and in particular some linguistic markers such as by the way can indicate to the addressees that the speaker is changing the topic. Removing these markers would enable us to assess their influence on mutual understanding.

A topic can change gradually in a conversation or more abruptly. In the first case, each intervention slightly changes the topic and thus helps understanding what is discussed. In the second case, the last interventions can help one notice that the previous topic has been wrapped up which licenses moving to a new one. We would like to see how people cope with missing some parts of the conversation, for instance by preventing one of the participants to receive some of the messages.

The meaning of some contributions can completely change if a piece of them is altered. For example, if someone is trying to describe an image with a cat but every time they use the word 'cat' it is replaced by 'dog', the final message will be completely different. The addressee would picture a different scene in their head and have a different perception of the at-hand topic from what the speaker expected. A message could also become complicated to understand if it contains an argument where one of the premise has been modified. If one receives the message "I need to buy gloves because it is getting so warm outside" instead of "I need to buy gloves because it is getting so cold outside", their interpretation can be unpredictable. They could believe the sender was being ironic, that they made a mistake, or potentially have an interpretation according to which buying gloves when it gets warm makes sense. We would like to observe the way people interpret messages where a core information has been modified, and in particular if they realise that they have a different understanding of their current discussion or if both speakers try to accommodate the other messages by coming up with a fitting interpretation.

In parallel to these phenomena, we would be interested in understanding how, after how long and with what motivation people shift the topic. This can only be done if the participants create topic shifts on their own as pushing them to change the topic would create a different phenomenon where one has specific reasons to change the current topic.

Tasks

This project aims to collect dialogues where participants perform a task involving various subtopics and to analyse the topic shifts and the different phenomena we discussed above.

One such task could be to discuss the ethical dilemma of the balloon task [START_REF] Breitholtz | Reasoning in Multiparty Dialogue Involving Patients with Schizophrenia[END_REF] where participants are asked to choose which of the four passengers of a hot air balloon to throw away, given that they would crash and all die if no one is sacrificed. The subtopics can there be defined by the different arguments in favour or against a passenger. We selected this task because its focus provides a manageable scope for conversation. . We could create more or less obvious topic shifts by inserting a fake message discussing one of the passengers when another one was discussed, or a message with a different argument from the one currently discussed. We could also create disruption by changing the passenger discussed in certain messages without notification.

A second task where the participants are supposed to cooperate to reach a common goal but where one of them could have hidden intentions would create interactions as both speakers would try to convince the other of their good intentions. Such a task could be realised through a game where the players both own a set of cards representing the wires of a bomb they are trying to disarm. The players know their own cards but not the ones of the other player. Disarming the bomb means cutting the wires in a certain order and thus returning the cards in a certain order. Discussing which card to return every turn would represent a topic. Inserting messages on a specific card or changing the colours that are discussed could create the phenomena discussed in Section 3.

Our last task is a description task inspired from the PhotoBook task [START_REF] Haber | The PhotoBook dataset: Building common ground through visually-grounded dialogue[END_REF]. In this task both participants receive a set of images and they have to determine which one of them they have in common. Describing one image could be considered a sub-topic here. Again, this task provides a manageable scope and should limit the amount of personal information shared by the participants. The modifications of the messages could consist in swapping one entity for another, and we could create more or less clear topic shifts by adding messages related to a different image from the one currently discussed.

All three tasks are designed to prevent the participants from sharing personal information and engaging on personal topics [START_REF] Amblard | L'impossibilité de l'anonymat dans le cadre de l'analyse du discours[END_REF]. Our goal is to observe the different phenomena described in Section 3 while minimising the impact of our manipulations on the content of the interventions.

Experimental Setup

Our goal is to gather conversations for the three tasks described in Section 4 where we manipulate some of the messages to observe the phenomenon discussed in Section 3. Table 1 describes the different manipulations we imagined for all the tasks and phenomena.

In total we have three tasks for four phenomena to observe. We also need one control setting for each task which makes fifteen different experiments. Our goal is to collect about 300 conversations in total (about 20 per experiment setting) where there would be three groups:

• about 30 conversations with first-time participants;

• about 90 conversations with participants joining in for the second, third or fourth time;

• about 180 conversations with 'trained' participants who have already taken part in the experiments more than 4 times. We would then annotate the dialogues with the topics but also the type of topic shifts. A topic shift can be more or less brutal and one of our goal is to define a scale where the most abrupt changes can be annotated with e.g. "explicitly marked" and "due to external factors". We also want to annotate the Questions Under Discussion (QUDs) [START_REF] Kuppevelt | Discourse structure, topicality and questioning[END_REF][START_REF] Ginzburg | Dynamics and the semantics of dialogue[END_REF] in order to compare the granularity that topics and QUDs encode.

Conclusion

This paper describes a corpus collection experiment aiming at creating a resource to analyse how people understand each other in more chaotic environments. Our assumption is that speakers are able to maintain interaction in spite of partial or distorted information.

We would like to gather data in three languages: English, French, and Swedish. On top of the contribution in terms of language resources, this would be beneficial to compare how people handle topic shifts in different languages.

The analyses of this corpus would enable us to identify topic shift mechanisms and determine which ones are more easily understandable. This would participate in incorporating topics to formal dialogue models but also in improving language models when it comes to understanding less obvious topic shifts. Our goal in the future is to create a proximity measure to identify topic shifts, which could for instance help find off-topic and potentially harmful contributions in dialogue-like interactions such as social media ones.

Figure 1 :

 1 Figure 1: Possible manipulations of the messages with the ChatTool

Table 1 :

 1 Manipulations of the conversations in the different tasks

	to more or less clear TS	Add message on an-other passenger / with another argument	Add message on an-other card	Add message on a very different image from the one discussed / on a similar image
	Influence of TS markers	Gather a list of markers and delete them from messages
	Missing information	Delete some arguments Delete some arguments	Delete some messages with a '?'
	Modified messages	Change the passenger discussed	Change the colour of the discussed wire	Modify some entities
	Control		No outside interventions


Siyana Pavlova

Maxime Amblard

Bruno Guillaume

A Layered Approach to Semantic Representation

Keywords:

Une approche stratifiée pour la représentation sémantique

Nous présentons une première version de notre approche de la représentation sémantique. Bien qu'il existe de nombreux formalismes sémantique, parvenir à rassembler plusieurs informations sémantiques tout en restant lisible reste une tâche difficile. Nous proposons de nous appuyer sur la structure prédicat-argument (PA) utilisée dons les AMR et d'étendre la représentation avec de nouveux type de sommets et d'arrêtes. Nous introduisons la notion de "caractéristiques" qui représente des phénomènes sémantiques nouvellement encodés sous forme de couches. Des arrêtes font le lien avec les caractéristiques sémantiques et la structure PA, ou dans le cas de phénomènes sémantiques en interaction avec d'autres caractéristiques. Notre approche permet de conserver la traditionnelle structure PA et de se focaliser sur des phénomènes particuliers et leurs interactions. Un avantage explicite est aussi de permettre de rendre compte de phénomènes complexes comme la portée (négation, quantification, etc.).

Introduction

Current semantic representation formalisms can be compared across various aspects, as shown in the literature [START_REF] Abend | The state of the art in semantic representation[END_REF][START_REF] Žabokrtský | Sentence meaning representations across languages: What can we learn from existing frameworks[END_REF]Pavlova et al., 2023b). In this work, we want to highlight distinctions in terms of their ability to encode various semantic phenomena, and their readability. Thus, we split formalisms into two broad categories -logic-based [START_REF] Kamp | From discourse to logic: Introduction to modeltheoretic semantics of natural language, formal logic and discourse representation theory[END_REF][START_REF] Montague R | English as a formal language[END_REF], and graph-based [START_REF] Banarescu | Abstract Meaning Representation for sembanking[END_REF][START_REF] Abend | Universal Conceptual Cognitive Annotation (UCCA)[END_REF][START_REF] White | Universal decompositional semantics on universal dependencies[END_REF]. Logic-based formalisms tend to be powerful in terms of encoding, being able to express phenomena such as quantifier scope, but are not easy to read and interpret without prior training in Logic. Graph-based ones, on the other hand, are easier to read and annotate, but often lack when it comes to expressing scope or being compositional. Admittedly, some graph-based formalisms, such Minimal Recursion Semantics (MRS) [START_REF] Copestake | Minimal recursion semantics: An introduction[END_REF] are able to encode a large set of phenomena. However, the way this is realised still makes the formalism difficult to read. Similarly, logic formulas can be represented as graphs. However, interpreting those still requires an understanding of Logic.

Many of the more recent graph-based formalisms have been proposed with large-scale annotation in mind, Abstract Meaning Representation (AMR) [START_REF] Banarescu | Abstract Meaning Representation for sembanking[END_REF] being the most popular one. However, this comes at the expense of not annotating various phenomena, such as temporal information, definiteness, plurality, etc. Uniform Meaning Representation [START_REF] Van Gysel | Designing a uniform meaning representation for natural language processing[END_REF], which can be seen as a standalone formalism, puts together a number of AMR extensions, achieving a rather more powerful representation than AMR alone. However, as can been in Figure 1 of [START_REF] Zhao | UMR-writer: A web application for annotating uniform meaning representations[END_REF], since all semantic phenomena are added as nodes and edges of the same status as those in the original AMR structure, the representation becomes rather messy for longer texts.

Our goal with this work is to create a formalism which is a common ground between the two -easy to read and annotate, but also able to encode multiple semantic phenomena, without cluttering the structure. Furthermore, we want to keep each phenomenon separate from the central predicate-argument structure in order to keep the structure more organised, and to allow for the independent exploration of different phenomena. Thus, we propose a way to modify and extend an AMR-inspired graph representation by adding explicit layers to the structure. Each layer is designed to encode a different semantic phenomenon, allowing a straightforward way to "switch off" the layer. This avoids cluttering of the representation when certain phenomena are not the focus of a given task.

Proposal

We present here our proposal to extend graph structures in order to encode various semantic phenomena in specifically dedicated layers. For this purpose, we add a number of node types and relation types. We will explore these with the help of the two examples shown in Figure 1: for the sentence "I could not find the newspaper" (Figure 1a) with negation and modality, and "Every cow ate an apple" (Figure 1b), with the usual scope ambiguity.

The predicate-argument structure of each sentence is represented as a standard graph (the part inside the dashed rectangle), with nodes (from a set of nodes V) and edges (from a set of edges E). The nodes are predicates and concepts. The edges are argument roles and are labeled. The predicates and the argument roles have been taken from PropBank [START_REF] Palmer | The Proposition Bank: An annotated corpus of semantic roles[END_REF]. This is the standard structure we see in AMR and AMR-derived formalisms.

However, we restrict the usual AMR notation to the PA structure of the main predicate of a phrase. By doing this we assume to follow a neo-davidsonian representation of semantics [START_REF] Davidson | The logical form of action sentences[END_REF][START_REF] Parsons | Events in the Semantics of English: A Study in Subatomic Semantics[END_REF]. In this line, we extend this by first adding a node S to represent the event itself (S1 in the two figures). For multiple events in the same sentence, we introduce a node of the same type for each event, as we will see in section 3.

Then, a set of features F is added, each representing a semantic phenomenon that we wish to encode in the structure. Each feature is linked to an event node. In Figure 1a, we represent temporality, modality and negation. In Figure 1b, in addition to temporality, we also encode quantification.

Next, we introduce a set of edges, E', which link a feature to a node from V, as we see with modal and find-01 in Figure 1a, or with temp and eat-01 in Figure 1b.

Figure 1a demonstrates that there are cases where it is not enough to link features only to nodes from V. In that sentence, it is not the finding event that is expressed in the past, but the speaker's ability to do so. Thus, we introduce another set of edges, which link a feature to an edge from E'. This is also useful for negation as we can see in the same sentence.

Finally, as demonstrated in Figure 1b, when more than one quantifier is present in a sentence, Figure 2: "I could not find the newspaper until you told me where it would suddenly appear." we might need to specify which one takes scope over which other ones. To be able to do this, in the quantification layer we introduce another type of edges, which can link an edge from E' to a node from V. Edges linking to and from features labeled or unlabeled, depending on the feature they are linked to.

One of the advantages of the thus described structure is that while various phenomena can be encoded, it is easy to choose to not represent a layer if the phenomenon it represents is not relevant for a given task. In Figure 1a, even though it is possible, we have chosen to not represent quantification.

Towards more complex examples

Let us now imagine a more complex example with multiple events. In Figure 2, we have our representation for the sentence "I could not find the newspaper until you told me where it would suddenly appear", where we have three events: finding, telling, and appearing.

As pointed out earlier, when dealing with multiple events, we choose to follow a neodavidsonian approach, treating each event separately. Thus, we introduce three event nodes, S1 for "find", S2 for "tell" and S3 for "appear". The word "until" expresses the fact that S1 was happening before S2, thus we employ the use of another set of edges, E S , which links event nodes and add one such edge between S1 and S2 with the label BEFORE. Edges in E S can be thought of as discourse relations, demonstrating our proposal's ability to go beyond semantics.

Using edges from E S is one of the ways we propose for handling subordinate clauses, specifically those introduced by subordinating conjunctions such as "because", "although", "until". However, some subordinating conjunctions can be directly linked as arguments of the predicate in the main clause, as we see with the example of "where it would suddenly appear", where the location represented by "where" is the ARG1 of the predicate tell-01 as per PropBank. Thus, while the "appearing" event is represented in its own box, one of its elements, namely, the location is linked to tell-01 by an edge from E.

As we see in S3's box, we also treat AMR's non-core arguments differently. These are optional modifiers that are not predicate-dependent. As such, we link them to the event node via a new feature, rather than the main predicate of that event. We can see with sudden being linked to S3 via the manner feature in Figure 2.

Finally, we have two instances of co-reference in this example, between "I" and "me" and between "newspaper" and "it". Our current approach is to link these with a pair of dashed edges, different in type from the edges in E, running between each instance of the co-referent. These edges are unlabeled.

With the example in this section, we demonstrate our proposal's versatility to encode various phenomena even for longer texts. The use of layers allows us to represent a wide range of semantic phenomena while keeping it easy to identify each of them in the overall structure. Representing each event separately allows us to maintain this aspect of the representation even for longer texts.

Discussion and Future Work

In this work, we do not provide all the definitions of the framework, but we have explored its ability to encode a lot of different phenomena and how they could interact in the representation, namely: temporality, aspect, modality, negation, quantifier scope, definiteness, plurality and generics. Thus, we believe our representation has the potential to encode the phenomena encoded by logic-based formalisms. However, with our proposal on how to visualise it, it is also more easy to read and annotate. Finally, the clear separation between phenomena allows for a more organised view and the possibility to "switch off" certain layers when irrelevant for a specific task.

As we have seen, edges linking to and from features can be labeled or unlabeled, depending on the feature they are connected to. The focus of this work so far has been the general structure of our proposal. As such, we have used only a simple set of classes for each feature: possibility and necessity for modal, past, present and future for temp. In the future, we intend to explore the lattice approach proposed in [START_REF] Van Gysel | Cross-linguistic semantic annotation: Reconciling the language-specific and the universal[END_REF] and utilised in UMR. Furthermore, as we can see with the example in Figure 2, while the three events all happen in the past, it is clear to a speaker that there is an order in which they happen -the inability to find the newspaper happens before the telling, while the appearance of the newspaper happens after it. This information is not captured by the current version of our proposal but will be the subject of future work, where we could utilise Reichenbach's notions of speech time, event time and reference time [START_REF] Reichenbach | Elements of Symbolic Logic[END_REF].

The next steps for this project are to give the formal definition of our proposal, expand the list of phenomena we encode, annotate a first exploration corpus, and provide annotation guidelines. This work will be followed by transformation experiments between the thus proposed formalism and other semantic representation formalisms in the line of our previous work (Pavlova et al., 2023a). Furthermore, to allow for this representation to be used for semantic parsing and tasks further up the NLP pipeline, we want to develop an equivalent textual representation in the spirit of PENMAN notation [START_REF] Matthiessen | Text generation and systemic-functional linguistics: experiences from english and japanese[END_REF].Finally, our work so far has been guided and informed by examples in English. For future version, we plan to take language other than English into account in order to make sure we are designing a universal framework.

  Figure 1: Representation for two sentences