Alloatti, F.

Cena, F.

Di Caro, L.

Ferrod, R.

Siragusa, G.

English

Institutional Research Information System University of Turin

Towards Mental Model-driven ConversationsFrancesca Alloattia,b, Federica Cenab, Luigi Di Carob, Roger Ferrodb andGiovanni SiragusabaCELI - Language Technology, Turin, ItalybUniversity of Turin, Torino, ItalyAbstractIn recent years conversation has become a key channel for human-computer interaction. Dialogue per-sonalization could result in an important aspect, making sense of users’ features when engaged in aconversation with a machine. A feature that has been properly taken into account is the user’s mentalmodel, a crucial aspect since it determines users’ expectations and the way they interact with a chatbot.In this position paper, we propose a theoretical framework that combines existing meta-mental models(behaviour-based and lexical-based ) in a computational model that can be used to automatically detectthe users’ mental model from the dialogues with a chatbot by exploiting Linguistic theory and MachineLearning techniques.Keywordsmental model, conversational agents, neural networks1. IntroductionRecent years have seen the rise of conver-sational agents (CAs) in everyday life: chat-bots that use written communications aboveall, but also vocal digital assistants. It is safeto say that conversation is becoming a keymode of human-computer interaction. How-ever, despite much recent success in naturallanguage processing and dialogue research,the communication between a human and amachine is still in its infancy. In this context,dialogue personalization could be the solu-tion to narrow part of the gap, making senseof users’ features (e.g., preferences, expertise,communication style, emotions, personality)when engaged in a conversation with a ma-chine. In this context, the users’ mental modelis yet to be properly explored, while it is aJoint Proceedings of the ACM IUI 2021 Workshops, April13–17, 2021, College Station, USA" francesca.alloatti@celi.it (F. Alloatti)© 2021 Copyright for this paper by its authors. Use permit-ted under Creative Commons License Attribution 4.0 Inter-national (CC BY 4.0).CEURWorkshopProceedingshttp://ceur-ws.orgISSN 1613-0073CEUR Workshop Proceedings(CEUR-WS.org)crucial aspect since it determines users’ ex-pectations and the way they interact with achatbot.The notion of mental model was originallypostulated by the psychologist Kenneth Craik [1]as a small-scale model of how the world works.This model is used to anticipate events, toreason, to generate explanation. A mentalmodel can be seen as a reasoning mechanismin a person’s working memory or as a set ofbeliefs and understandings that help users’decisions and interaction with world [2].mental model is often confused with thenotion of User Model, since both produce re-ciprocal expectation. mental model are theexpectations users have about a computer’sbehaviour [3], while the User Model is therepresentation the system has of a user’s [4].The fundamental distinction is that a mentalmodel can be found in the mind of people,while User Model resides inside a computer.mental model has been historically appliedto Intelligent Tutoring System (ITS) field , wheredetecting mental model shifts during learn-ing represents an important step in diagnos-ing ineffective learning and intervening byproviding appropriate feedback. Another fieldwhere mental model has extensively studiedis Human-computer Interaction (HCI), whereit has been defined as “internal representa-tions of a system” [5], which “are formed throughknowledge (education), experience or a com-bination of the two” [6], and thus can differfrom the institutionalized, legitimated con-ceptions held by experts.It is extremely important to understand themental model that people use in the interac-tion in order to design more effective inter-faces for the users [5]. This is especially truefor conversational interfaces. For instance, ifa chatbot is aware of the user’s mental model,it may change its answers depending on theperson’s beliefs about the chatbot in order tomake them more understandable and helpful.Since the mental model is not directly ob-servable, the challenge is how to understandit. Usually, qualitative user studies have beenperformed to this aim [7, 8, 9]. Instead, wepropose to combine existing meta-mental mod-els (behaviourally-based [8] and lexical-basedfeatures [10]) in a computational fashion thatcan be used to automatically detect the users’mental model from the dialogues with a chat-bot, exploiting Linguistic theory and MachineLearning techniques.2. Background: Users’Mental Model DetectionBecause mental models are not directly ob-servable, it is necessary to use some evidenceto infer their features (explanations, reactiontime, questions..), usually exploiting qualita-tive techniques (interviews, focus groups, etc).For example, [7] takes into account 14 semi-structured interviews conducted with exist-ing users of CA systems (Siri, Cortana) andpresent four key areas where current systemsfail to support effective user interaction. Inthe majority of cases users were unable tomake accurate judgments about system ca-pability. Users had poor mental models ofhow their agents worked and these were re-inforced through a lack of meaningful feed-back mechanisms." Extended Abstracts of the 2018 CHI Con-ference on Human Factors in Computing Sys-tems. 2018. Another example is [8], who con-ducted an experiment where 20 novice usershad to complete 5 tasks (factual, instrumen-tal, controversial, predictive) while interact-ing with a Google Home device, and askedthem to think aloud as they completed thosetasks.Two mental models emerged from verbalstrategies, which seemed to indicate differingapproaches in the participants’ informationseeking behavior: i) Push model. People whoemployed the push model tried to ”push” thesystem to give them more information by ex-plaining to the machine their needs. This strat-egy is derived from the experience in a nor-mal conversation with a human, which meansthat they believe articulating a specific per-sonal information or a desire makes the mis-communication clear; ii) Pull model. the par-ticipants tried to ”pull” information from themachine. They asked questions to see whetherthe system was able to draw out an answerfor a particular type of information.Only few work addresses the challengingproblem of automatically detecting the men-tal model. Those studies usually pertain tothe learning domain, where the mental modelcan be seen as the "students’ level of under-standing of a topic" [11]. They view the taskof detecting the student mental models as astandard classification problem. The generalapproach is to combine textual features, whichare automatically extracted, with supervisedmachine learning algorithms to automaticallyderive classifiers from expert-annotated data.For instance, [11] used content-based over-lap methods, cohesion analysis of text, andword-weight based representations (like if-idf). They were combined with machine learn-ing algorithms in order to automatically in-fer the underlying parameters. The authorsevaluated their approach comparing the meth-ods’ predictions with human judgments.3. Our proposedMetamodel of MentalModelWe propose to join different findings frommental model detection studies in a uniqueframework. The framework would be sus-tained by machine learning techniques; theywould automatically compute the users’ men-tal model by taking into account multiple fea-tures. Some of the features are behaviourally-based [8], while others take into account users’linguistic conduct during the human-machinedialogue [10].In our perspective, users can be located alonga spectrum: on one end, stands the compre-hension of the system as it really is; it couldbe characterized as the mental model of thosewho developed the agent, and therefore wecall these users Developers. On the oppositeend, stand those who may be called Primi-tives. The differentiation between Developersand Primitives is exclusively linguistic: basedon the lexicon they employ while interactingwith the machine or the syntactic structureof their sentences, it is possible to computea mental model ”score” that situates a useralong the spectrum.However, in order to merge the linguisticmental model detection with the behaviouralone, it’s necessary to formalize the elementsthat lead to a Push or Pull model. [8] ob-tained the two models by observing the inter-actions and the verbal strategies. In our case,we propose a tagging system of the users’ ut-terances that can lead to the identification ofthe appropriate model.3.1. Automatic Detection of theMental ModelThe true challenge is to transpose the find-ings and theories about the mental model to-wards an automatic approach, i.e. an AI-drivenframework that is able to compute the users’mental model autonomously, on the fly, fromparameters that were clearly set. To achievethat, we identified some features of the dia-logue that can help compute it:• Primitive orDevelopermentalmodel.So-called Developers present compliancewith the chatbot instructions; they avoidcontextual references or out-of-contextrequests, since they are aware of themachine’s capabilities. Primitives, onthe other hand, will employ a more human-like form of conversation. They mayuse polite or formulaic expressions, orrefer to world elements that are unknownto a machine.• Pull or Push mental model. Insteadof looking for specific linguistic features,in this case the whole utterance getstagged with a single label. Labels couldexpress the kind of request the user ismaking: for instance, is she trying torephrase her request in order to pullthe right information? Or is she tryingto explain to the machine her needs inplain words? The distribution of ”Pushtags” or Pull ones in a conversation couldthen reveal the behavioural mental modelof that user.To capture the user’ mental model, a singledataset of human-machine dialogues could bemanipulated and analyzed: first, each utter-ance can get marked with a Push or Pull tag;then, their linguistic features can be capturedin order to situate a user along the Developerand Primitives spectrum. Values from bothapproach are taken into account to producea customization of the system.We plan to model the problem by buildinga modular architecture capable of extractingdifferent features. For example, if it is neces-sary to tag the single words of a message, itis possible to refer to the sequence labelingproblem and its best known techniques, suchas Neural Networks and Conditional RandomFields [12]. Similarly, Recurrent and Convo-lutional Neural Network models can be usedin the analysis of the context that surroundswords, thus capturing the most complex fea-tures related to the mental model. Psycholin-guistic features such as greeting forms, chat-bot anthropomorphization and out-of-contextrequests can be easily unveiled by attentionmechanisms [13]. The latter ones, indeed, dy-namically focus on words that are relevantfor the task prediction. Finally, since it is nec-essary to aggregate all the intermediate re-sults to generate a single score associated withthe conversation and therefore with the user,we believe that an inter-attention technique[14, 15] is particularly suitable. The idea is tocapture inter-related word features betweenutterances and select the relevant ones via aconvolutional model to predict the score.Once the various modules have been de-fined, it will be possible to integrate them intoa single Neural Network model. The trainingcan then continue separately or following themulti-tasking learning approach that allowsto learn multiple tasks simultaneously.4. ConclusionThis work presents a theoretical frameworkto enhance task-oriented agents-human dia-logues by taking the user mental model intoaccount. The framework exploits linguistictheory disciplines, human-computer interac-tions findings [8] and machine learning tech-niques, such as advanced in neural networks.The objective is to personalize the dialoguetaking the user model into account. For ex-ample, it could be possible to provide addi-tional explanations about how the chatbot worksand its goals to a user with a primitive mentalmodel. At the same time, the chatbot couldconsider other user’s features, such as her ex-pertise, in order to adapt the conversation,for example providing explanations of con-cepts in relation to the level of knowledge ofthe user, changing the style and terminologyaccording to user’s expertise or helping theuser in understanding her limits and poten-tialities (e.g. suggesting what the user mayask, or explaining what Angie is not able to).Moreover, this information can be combinedwith failure in conversation, in order to per-form personalized recovery strategies that con-siders both user mental model and expertise.For example, for some users it may be viableto provide more information through linksand external webpages, while for others it maybe more practical to refer them to human agents.We are currently experimenting with the clas-sification task. We obtained promising re-sults with a proprietary dataset. Future workwill include the extension of the task to opendatasets for the sake of reproducibility. It isworth noting that although the whole systemcould be applied to all sort of task-orientedagents, user’s profiling shall always dependon the specific context and requirements ofthe agent’s domain, specifically for the tech-nical expertise evaluation. The authors be-lieve that this strategy could significantly im-prove the intelligence of the system, withoutthe need to intervene on the neural dialoguemodel. The final goal would be to find newways to build dialogue system that are closerand more compliant to the actual mechanismsof the human mind, by integrating findingsfrom psycholinguistics and philosophy of themind into computer systems.References[1] K. J. W. Craik, The nature of explana-tion, volume 445, CUP Archive, 1952.[2] N. A. Jones, H. Ross, T. Lynam, P. Perez,A. Leitch, Mental models: an interdis-ciplinary synthesis of theory and meth-ods, Ecology and Society 16 (2011).[3] R. B. Allen, Mental models and usermodels, in: Handbook of human-computer interaction, Elsevier, 1997,pp. 49–63.[4] P. Brusilovsky, E. Millán, User modelsfor adaptive hypermedia and adaptiveeducational systems, in: The adaptiveweb, Springer, 2007, pp. 3–53.[5] D. A. Norman, Some observations onmental models, Mental models 7 (1983)7–14.[6] N. Staggers, A. F. Norcio, Mental mod-els: concepts for human-computer in-teraction research, International Jour-nal of Man-machine studies 38 (1993)587–605.[7] E. Luger, A. Sellen, " like having a re-ally bad pa" the gulf between user ex-pectation and experience of conversa-tional agents, in: Proceedings of the2016 CHI conference on human factorsin computing systems, 2016, pp. 5286–5297.[8] J. Cho, Mental models and home vir-tual assistants (hvas), in: Extended Ab-stracts of the 2018 CHI Conference onHuman Factors in Computing Systems,2018, pp. 1–6.[9] H. Candello, C. Pinhanez, F. Figueiredo,Typefaces and the perception of hu-manness in natural language chatbots,in: Proceedings of the 2017 CHI Confer-ence on Human Factors in ComputingSystems, 2017, pp. 3476–3487.[10] F. Alloatti, L. Di Caro, A. Bosca, Conver-sation analysis, repair sequences andhuman computer interaction - a the-oretical framework and an empiricalproposal of action, 2020. Accepted atThe Fourth Workshop on Reasoningand Learning for Human-Machine Di-alogues (AAAI 2021).[11] R. Azevedo, A. Witherspoon,A. Chauncey, C. Burkett, A. Fike,Metatutor: A metacognitive tool forenhancing self-regulated learning, in:2009 AAAI Fall symposium series,2009.[12] G. Lample, M. Ballesteros, S. Subrama-nian, K. Kawakami, C. Dyer, Neuralarchitectures for named entity recog-nition, in: Proceedings of the 2016Conference of the North AmericanChapter of the Association for Compu-tational Linguistics: Human LanguageTechnologies, Association for Compu-tational Linguistics, San Diego, Cali-fornia, 2016, pp. 260–270. URL: https://www.aclweb.org/anthology/N16-1030.doi:10.18653/v1/N16-1030.[13] D. Bahdanau, K. Cho, Y. Bengio, Neuralmachine translation by jointly learningto align and translate, arXiv preprintarXiv:1409.0473 (2014).[14] Y. Tay, A. T. Luu, S. C. Hui, Com-pare, compress and propagate: Enhanc-ing neural architectures with alignmentfactorization for natural language in-ference, in: Proceedings of the 2018Conference on Empirical Methods inNatural Language Processing, 2018, pp.1565–1575.[15] B. Tian, Y. Zhang, J. Wang, C. Xing, Hi-erarchical inter-attention network fordocument classification with multi-tasklearning., in: IJCAI, 2019, pp. 3569–3575.

Towards Mental Model-driven Conversations

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Towards Mental Model-driven Conversations

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