Relevant bibliographies by topics / Chatbot dialogue

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Chatbot dialogue'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Chatbot dialogue"

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Fang, Jiyang. "Analysis on Chatbot Performance based on Attention Mechanism." Highlights in Science, Engineering and Technology 39 (April 1, 2023): 151–56. http://dx.doi.org/10.54097/hset.v39i.6517.

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The chatbot is a way to imitate the dialogue between people through natural language, enabling human beings to communicate with machines more naturally. The chatbot is a prevalent natural language processing task (NLP) because it has broad application prospects in real life. This is also a complex task involving many natural language processing tasks that must be studied. The chatbot is an intelligent dialogue system that can simulate human dialogue to achieve online guidance and support. The main work of this paper is to summarize the chatbot's academic background and research status and introduce the Cornell Movie-Dialogs Corpus dataset. The methods of artificial intelligence and natural language processing are outlined. Two attention mechanisms used to improve neural machine translation (NMT) are discussed. Finally, this paper tests the performance of chatbots under the influence of N_ITERATION and data scale summarizes the relevant optimization strategies and makes a prospect for the future of chatbots. The main work of this paper is to test the performance of the proposed method under different experimental Settings, including dialog templates, adjusting the amount of training data, and to adjust the number of iterations. The results show that the chatbot's vocabulary changes with N_ITERATION and that increasing the data in the training dataset improves the chatbot's understanding.
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Sagstad, Mari Haaland, Nils-Halvdan Morken, Agnethe Lund, Linn Jannike Dingsør, Anne Britt Vika Nilsen, and Linn Marie Sorbye. "Quantitative User Data From a Chatbot Developed for Women With Gestational Diabetes Mellitus: Observational Study." JMIR Formative Research 6, no. 4 (2022): e28091. http://dx.doi.org/10.2196/28091.

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Background The rising prevalence of gestational diabetes mellitus (GDM) calls for the use of innovative methods to inform and empower these pregnant women. An information chatbot, Dina, was developed for women with GDM and is Norway’s first health chatbot, integrated into the national digital health platform. Objective The aim of this study is to investigate what kind of information users seek in a health chatbot providing support on GDM. Furthermore, we sought to explore when and how the chatbot is used by time of day and the number of questions in each dialogue and to categorize the questions the chatbot was unable to answer (fallback). The overall goal is to explore quantitative user data in the chatbot’s log, thereby contributing to further development of the chatbot. Methods An observational study was designed. We used quantitative anonymous data (dialogues) from the chatbot’s log and platform during an 8-week period in 2018 and a 12-week period in 2019 and 2020. Dialogues between the user and the chatbot were the unit of analysis. Questions from the users were categorized by theme. The time of day the dialogue occurred and the number of questions in each dialogue were registered, and questions resulting in a fallback message were identified. Results are presented using descriptive statistics. Results We identified 610 dialogues with a total of 2838 questions during the 20 weeks of data collection. Questions regarding blood glucose, GDM, diet, and physical activity represented 58.81% (1669/2838) of all questions. In total, 58.0% (354/610) of dialogues occurred during daytime (8 AM to 3:59 PM), Monday through Friday. Most dialogues were short, containing 1-3 questions (340/610, 55.7%), and there was a decrease in dialogues containing 4-6 questions in the second period (P=.013). The chatbot was able to answer 88.51% (2512/2838) of all posed questions. The mean number of dialogues per week was 36 in the first period and 26.83 in the second period. Conclusions Frequently asked questions seem to mirror the cornerstones of GDM treatment and may indicate that the chatbot is used to quickly access information already provided for them by the health care service but providing a low-threshold way to access that information. Our results underline the need to actively promote and integrate the chatbot into antenatal care as well as the importance of continuous content improvement in order to provide relevant information.
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Tsai, Wan-Hsiu Sunny, Yu Liu, and Ching-Hua Chuan. "How chatbots' social presence communication enhances consumer engagement: the mediating role of parasocial interaction and dialogue." Journal of Research in Interactive Marketing 15, no. 3 (2021): 460–82. http://dx.doi.org/10.1108/jrim-12-2019-0200.

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PurposeThis study presents one of the earliest empirical investigations on how brand chatbots' anthropomorphic design and social presence communication strategies may improve consumer evaluation outcomes via the mediators of parasocial interaction and perceived dialogue.Design/methodology/approachThis study employs a 2 (high vs. low social presence communication) by 2 (anthropomorphic vs. non-anthropomorphic bot profile) between-subject experimental design to evaluate how chatbots' high social presence communication and anthropomorphic profile design may enhance perceptions of parasocial interactions and dialogue with the chatbot, which in turn drive user engagement, interaction satisfaction and attitude toward the represented brand.FindingsThe influences of chatbots' high social presence communication on consumer engagement outcomes are mediated by perceived parasocial interaction and dialogue. Additionally, chatbots' anthropomorphic profile design can boost the positive effects of social presence communication via the psychological mediators.Originality/valueThis study advances the interactive marketing literature by focusing on an emerging interactive technology, chatbots. Additionally, distinct from prior chatbot studies that focused on the utilitarian use of chatbots for online customer support, this study not only examines which factors of chatbot communication and profile design may drive chatbot effectiveness but also examines the mechanism underlying the messaging and design effects on consumer engagement. The findings highlight the mediating role of interpersonal factors of parasocial interaction and perceived dialogue.
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Lin, Chien-Chang, Anna Y. Q. Huang, and Stephen J. H. Yang. "A Review of AI-Driven Conversational Chatbots Implementation Methodologies and Challenges (1999–2022)." Sustainability 15, no. 5 (2023): 4012. http://dx.doi.org/10.3390/su15054012.

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A conversational chatbot or dialogue system is a computer program designed to simulate conversation with human users, especially over the Internet. These chatbots can be integrated into messaging apps, mobile apps, or websites, and are designed to engage in natural language conversations with users. There are also many applications in which chatbots are used for educational support to improve students’ performance during the learning cycle. The recent success of ChatGPT also encourages researchers to explore more possibilities in the field of chatbot applications. One of the main benefits of conversational chatbots is their ability to provide an instant and automated response, which can be leveraged in many application areas. Chatbots can handle a wide range of inquiries and tasks, such as answering frequently asked questions, booking appointments, or making recommendations. Modern conversational chatbots use artificial intelligence (AI) techniques, such as natural language processing (NLP) and artificial neural networks, to understand and respond to users’ input. In this study, we will explore the objectives of why chatbot systems were built and what key methodologies and datasets were leveraged to build a chatbot. Finally, the achievement of the objectives will be discussed, as well as the associated challenges and future chatbot development trends.
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Gregorcic, Bor, and Ann-Marie Pendrill. "ChatGPT and the frustrated Socrates." Physics Education 58, no. 3 (2023): 035021. http://dx.doi.org/10.1088/1361-6552/acc299.

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Abstract We present a case study of a conversation between ourselves and an artificial intelligence-based chatbot ChatGPT. We asked the chatbot to respond to a basic physics question that will be familiar to most physics teachers: ‘A teddy bear is thrown into the air. What is its acceleration in the highest point?’ The chatbot’s responses, while linguistically quite advanced, were unreliable in their correctness and often full of contradictions. We then attempted to engage in Socratic dialogue with the chatbot to resolve the errors and contradictions, but with little success. We found that ChatGPT is not yet good enough to be used as a cheating tool for physics students or as a physics tutor. However, we found it quite reliable in generating incorrect responses on which physics teachers could train assessment of student responses.
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Kumar, Kartik. "An Educational Chatbot Using AI in Radiotherapy." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34122.

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The surge in demand for information in cancer centers and hospitals, particularly during the pandemic, overwhelmed the limited manpower available. To address this challenge, there arose a need to develop an educational chatbot tailored for diverse user groups in the field of radiotherapy, including patients and their families, the general public, and radiation staff. Objective: In response to the pressing clinical demands, the primary aim of this endeavor is to delve into the intricacies of designing an educational chatbot for radiotherapy using artificial intelligence.Methods: The chatbot is meticulously crafted using a dialogue tree and layered structure, seamlessly integrated with artificial intelligence functionalities, notably natural language processing (NLP). This adaptable chatbot can be deployed across various platforms, such as IBM Watson Assistant, and embedded in websites or diverse social media channels.Results: Employing a question-and-answer methodology, the chatbot adeptly engages users seeking information on radiotherapy, presenting an approachable and reassuring interface. Recognizing that users, often anxious, may struggle to articulate precise questions, the chatbot facilitates the interaction by offering a curated list of questions. The NLP system augments the chatbot's ability to discern user intent, ensuring the provision of accurate and targeted responses. Notably, the study reveals that functional features, including mathematical operations, are preferred in educational chatbots, necessitating routine updates to furnish fresh content and features.Conclusions: The study culminates in the affirmation that leveraging artificial intelligence facilitates the creation of an educational chatbot capable of disseminating information to users with diverse backgrounds in radiotherapy. Furthermore, the importance of rigorous testing and evaluation, informed by user feedback, is emphasized to iteratively enhance and refine the chatbot's performance. Keywords: AI, machine learning, NLP, chatbot, radiotherapy, IoT, healthcare.
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Li, Jingquan. "Security Implications of AI Chatbots in Health Care." Journal of Medical Internet Research 25 (November 28, 2023): e47551. http://dx.doi.org/10.2196/47551.

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Artificial intelligence (AI) chatbots like ChatGPT and Google Bard are computer programs that use AI and natural language processing to understand customer questions and generate natural, fluid, dialogue-like responses to their inputs. ChatGPT, an AI chatbot created by OpenAI, has rapidly become a widely used tool on the internet. AI chatbots have the potential to improve patient care and public health. However, they are trained on massive amounts of people’s data, which may include sensitive patient data and business information. The increased use of chatbots introduces data security issues, which should be handled yet remain understudied. This paper aims to identify the most important security problems of AI chatbots and propose guidelines for protecting sensitive health information. It explores the impact of using ChatGPT in health care. It also identifies the principal security risks of ChatGPT and suggests key considerations for security risk mitigation. It concludes by discussing the policy implications of using AI chatbots in health care.
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Černý, Michal. "Educational Psychology Aspects of Learning with Chatbots without Artificial Intelligence: Suggestions for Designers." European Journal of Investigation in Health, Psychology and Education 13, no. 2 (2023): 284–305. http://dx.doi.org/10.3390/ejihpe13020022.

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Chatbots without artificial intelligence can play the role of practical and easy-to-implement learning objects in e-learning environments, allowing a reduction in social or psychological isolation. This research, with a sample of 79 students, explores the principles that need to be followed in designing this kind of chatbot in education in order to ensure an acceptable outcome for students. Research has shown that students interacting with a chatbot without artificial intelligence expect similar psychological and communicative responses to those of a live human, project the characteristics of the chatbot from the dialogue, and are taken aback when the chatbot does not understand or cannot help them sufficiently. The study is based on a design through research approach, in which students in information studies and library science interacted with a specific chatbot focused on information retrieval, and recorded their experiences and feelings in an online questionnaire. The study intends to find principles for the design of chatbots without artificial intelligence so that students feel comfortable interacting with them.
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Shawar, Bayan Abu, and Eric Steven Atwell. "Using corpora in machine-learning chatbot systems." International Journal of Corpus Linguistics 10, no. 4 (2005): 489–516. http://dx.doi.org/10.1075/ijcl.10.4.06sha.

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A chatbot is a machine conversation system which interacts with human users via natural conversational language. Software to machine-learn conversational patterns from a transcribed dialogue corpus has been used to generate a range of chatbots speaking various languages and sublanguages including varieties of English, as well as French, Arabic and Afrikaans. This paper presents a program to learn from spoken transcripts of the Dialogue Diversity Corpus of English, the Minnesota French Corpus, the Corpus of Spoken Afrikaans, the Qur'an Arabic-English parallel corpus, and the British National Corpus of English; we discuss the problems which arose during learning and testing. Two main goals were achieved from the automation process. One was the ability to generate different versions of the chatbot in different languages, bringing chatbot technology to languages with few if any NLP resources: the corpus-based learning techniques transferred straightforwardly to develop chatbots for Afrikaans and Qur'anic Arabic. The second achievement was the ability to learn a very large number of categories within a short time, saving effort and errors in doing such work manually: we generated more than one million AIML categories or conversation-rules from the BNC corpus, 20 times the size of existing AIML rule-sets, and probably the biggest AI Knowledge-Base ever.
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Bodapati, Mrs Nagaeswari. "Campus Companion : Creating a Supportive Chat – Assistant for Students." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29939.

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This article explores the creation and usage of chatbots, or intelligent conversational agents, for online communication. Python and other machine learning methods are employed in the chatbot's design. To be more explicit, TensorFlow/Keras is used for natural language processing, MySQL is utilized for database administration, and Flask is used for web hosting. The initiative attempts to accomplish its goals by enhancing the user experience and fostering more effective communication on the website. Using massive datasets, the chatbot will be educated to comprehend users' objectives and offer replies that fit with them. Natural language processing technologies, including voice recognition, natural language interpretation, and natural language production, will be used to foster natural discussions. A number of machine learning approaches, such as transformers, attention processes, and recurrent neural networks, will be investigated in order to classify intent and deliver replies. The objective is to construct a powerful and versatile chatbot that can grasp arguments in their context, preserve the debate's status, and answer in a manner that sounds human. Assessment criteria such as accuracy, recall, and precision will be utilized to develop and adapt the chatbot. Numerous businesses, including education, entertainment, customer service, and other disciplines, may profit from the employment of this chatbot. The implementation of intelligent conversational interfaces on websites has the potential to greatly enhance user experience. Keywords— Chatbot, Flask, MySQL, TensorFlow, Keras, Website Interaction, Natural Language Processing (NLP), Conversational AI, Intent Classification, Response Generation, Machine Learning Models, Recurrent Neural Networks (RNNs), Transformer Models, Attention Mechanisms, User Experience (UX), Contextual Conversations, Dialogue State Management, Human-like Responses, Customer Service Automation, Educational Chatbot, Entertainment Applications.
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Dissertations / Theses on the topic "Chatbot dialogue"

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Roghult, Alexander. "Chatbot trained on movie dialogue." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-157637.

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A chatbot is a computer program that engages in written or spoken conversation with a human user. This project aims to investigate the possibility of training a chatbot in using movie dialogue in generating the response. Movie dialogue can be found in both movie scripts as well as subtitles, though using subtitles is much easier as they follow a special formatting. Using one subtitle as a response to each word found in the preceding subtitle, the implemented chatbot links together subtitles. The responses are stored in a frequency distribution table that maps each word to all responses found. Though the responses generated by the chatbot were not desirable, the responsese tmost often contained responses which would be more fitting. The result drawn from hisis that ,with further work and improvement,the chatbot could perform acceptably
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Bouguelia, Sara. "Modèles de dialogue et reconnaissance d'intentions composites dans les conversations Utilisateur-Chatbot orientées tâches." Electronic Thesis or Diss., Lyon 1, 2023. http://www.theses.fr/2023LYO10106.

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Les Systèmes de Dialogue (ou simplement chatbots) sont très demandés de nos jours. Ils permettent de comprendre les besoins des utilisateurs (ou intentions des utilisateurs), exprimés en langage naturel, et de répondre à ces intentions en invoquant les APIs (Interfaces de Programmation d’Application) appropriées. Les chatbots sont connus pour leur interface facile à utiliser et ils ne nécessitent que l'une des capacités les plus innées des humains qui est l'utilisation du langage naturel. L'amélioration continue de l'Intelligence Artificielle (IA), du Traitement du Langage Naturel (NLP) et du nombre incalculable de dispositifs permettent d'effectuer des tâches réelles (par exemple, faire une réservation) en utilisant des interactions basées sur le langage naturel entre les utilisateurs et un grand nombre de services.Néanmoins, le développement de chatbots est encore à un stade préliminaire, avec plusieurs défis théoriques et techniques non résolus découlant de (i) la variations d'énoncés dans les interactions humain-chatbot en libre échange et (ii) du grand nombre de services logiciels potentiellement inconnus au moment du développement. Les conversations en langage naturel des personnes peuvent être riches, potentiellement ambiguës et exprimer des intentions complexes et dépendantes du contexte. Les techniques traditionnelles de modélisation et d'orchestration de processus et de composition de services sont limitées pour soutenir de telles conversations car elles supposent généralement une attente a priori de quelles informations et applications seront accédées et comment les utilisateurs exploreront ces sources et services. Limiter les conversations à un modèle de processus signifie que nous ne pouvons soutenir qu'une petite fraction de conversations possibles. Bien que les avancées existantes dans les techniques de NLP et d'apprentissage automatique (ML) automatisent diverses tâches telles que la reconnaissance d'intention, la synthèse d'appels API pour prendre en charge une large gamme d'intentions d'utilisateurs potentiellement complexes est encore largement un processus manuel et coûteux.Ce projet de thèse vise à faire avancer la compréhension fondamentale de l'ingénierie des services cognitifs. Dans cette thèse, nous contribuons à des abstractions et des techniques novatrices axées sur la synthèse d'appels API pour soutenir une large gamme d'intentions d'utilisateurs potentiellement complexes. Nous proposons des techniques réutilisables et extensibles pour reconnaître et réaliser des intentions complexes lors des interactions entre humains, chatbots et services. Ces abstractions et techniques visent à débloquer l'intégration transparente et évolutive de conversations basées sur le langage naturel avec des services activés par logiciel<br>Dialogue Systems (or simply chatbots) are in very high demand these days. They enable the understanding of user needs (or user intents), expressed in natural language, and on fulfilling such intents by invoking the appropriate back-end APIs (Application Programming Interfaces). Chatbots are famed for their easy-to-use interface and gentle learning curve (it only requires one of humans' most innate ability, the use of natural language). The continuous improvement in Artificial Intelligence (AI), Natural Language Processing (NLP), and the countless number of devices allow performing real-world tasks (e.g., making a reservation) by using natural language-based interactions between users and a large number of software enabled services.Nonetheless, chatbot development is still in its preliminary stage, and there are several theoretical and technical challenges that need to be addressed. One of the challenges stems from the wide range of utterance variations in open-end human-chatbot interactions. Additionally, there is a vast space of software services that may be unknown at development time. Natural human conversations can be rich, potentially ambiguous, and express complex and context-dependent intents. Traditional business process and service composition modeling and orchestration techniques are limited to support such conversations because they usually assume a priori expectation of what information and applications will be accessed and how users will explore these sources and services. Limiting conversations to a process model means that we can only support a small fraction of possible conversations. While existing advances in NLP and Machine Learning (ML) techniques automate various tasks such as intent recognition, the synthesis of API calls to support a broad range of potentially complex user intents is still largely a manual, ad-hoc and costly process.This thesis project aims at advancing the fundamental understanding of cognitive services engineering. In this thesis we contribute novel abstractions and techniques focusing on the synthesis of API calls to support a broad range of potentially complex user intents. We propose reusable and extensible techniques to recognize and realize complex intents during humans-chatbots-services interactions. These abstractions and techniques seek to unlock the seamless and scalable integration of natural language-based conversations with software-enabled services
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Poltavchenko, Irina. "De l'analyse d'opinions à la détection des problèmes d'interactions humain-machine : application à la gestion de la relation client." Electronic Thesis or Diss., Paris, ENST, 2018. http://www.theses.fr/2018ENST0030.

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Motivée par le gain en popularité des chatbots prenant le rôle de conseillers sur les sites Web des entreprises, cette thèse s'attaque au problème de la détection des problèmes d’interaction entre un conseiller virtuel et ses utilisateurs sous l'angle de l'analyse des opinions et des émotions dans les textes. Cette thèse s’est déroulée dans le cadre d’une application concrète pour l’entreprise EDF et s'est appuyée sur le corpus du chatbot d'EDF. Ce corpus regroupe des expressions spontanées et riches, collectées dans les conditions écologiques (parfois appelées « in-the-wild »), difficiles à analyser de façon automatique, et encore peu étudiées. Nous proposons une typologie des problèmes d’interaction et faisons annoter une partie du corpus selon cette typologie, annotation dont une partie servira à l’évaluation du système. Le système de Détection Automatique des Problèmes d’Interaction (DAPI) développé lors de cette thèse est un système hybride qui allie l’approche symbolique et l’apprentissage non supervisé de représentation sémantique par plongements lexicaux (word embeddings). Le système DAPI a pour vocation d'être directement connecté au chatbot et de détecter des problèmes d’interaction en ligne, dès la réception d’un énoncé utilisateur. L'originalité de la méthode proposée repose sur : i) la prise en compte de l'historique du dialogue; ii) la modélisation des problèmes d’interaction en tant qu'expressions des opinions et des phénomènes reliés aux opinions spontanées de l'utilisateur vis-à-vis de l'interaction; iii) l'intégration des spécificités du langage web et « in-the-wild » comme des indices linguistiques pour les règles linguistiques; iv) recours aux plongements lexicaux de mots (word2vec) appris sur le grand corpus du chatbot non étiqueté afin de modéliser des similarités sémantiques. Les résultats obtenus sont très encourageants compte tenu de la complexité des données : F-score = 74,3%<br>This PHD thesis is motivated by the growing popularity of chatbots acting as advisors on corporate websites. This research addresses the detection of the interaction problems between a virtual advisor and its users from the angle of opinion and emotion analysis in the texts. The present study takes place in the concrete application context of a French energy supplier EDF, using EDF chatbot corpus. This corpus gathers spontaneous and rich expressions, collected in "in-the-wild" conditions, difficult to analyze automatically, and still little studied. We propose a typology of interaction problems and annotate a part of the corpus according to this typology. A part of created annotation is used to evaluate the system. The system named DAPI (automatic detection of interaction problems) developed during this thesis is a hybrid system that combines the symbolic approach and the unsupervised learning of semantic representation (word embeddings). The purpose of the DAPI system is to be directly connected to the chatbot and to detect online interaction problems as soon as a user statement is received. The originality of the proposed method is based on : i) taking into account the history of the dialogue ; ii) the modeling of interaction problems as the expressions of user spontaneous opinion or emotion towards the interaction ; iii) the integration of the web-chat and in-the-wild language specificities as linguistic cues for linguistic rules ; iv) use of lexical word embedding (word2vec) learned on the large untagged chatbot corpus to model semantic similarities. The results obtained are very encouraging considering the complexity of the data : F-score = 74.3%
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Westin, Anna. "Different recipient designs with dialogue partners : An experimental comparison between a Chatbot and a Human communication partner." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-151886.

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Chatbots are becoming more common in modern society, but there are almost no studies that explore both the differences and causes that divides human communication from communication with a chatbot. The aim of this thesis was to explore different recipient design people take when communicating with a human and a chatbot. A chatbot was built and an experiment was conducted that measured the performance and experience of the participants. A thematic analysis then found out causes for these experiences. The study focused on finding new differences in addition to exploring people’s boredom, frustration, understanding, repetition, and performance in a task. The study found differences and causes in people’s recipient design when communicating with a human compared to a chatbot, as well as differences in the performance of a task. Hopefully, this will help future research figure out solutions for the differences found.
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Asher, Natali. "A Warmer Welcome : Application of a Chatbot as a Facilitator for New Hires Onboarding." Thesis, Linnéuniversitetet, Institutionen för medieteknik (ME), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-65887.

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Despite being explored and constantly improved through the years, onboarding of new hires in corporate organizations has remained a challenge. Many of the issues can be linked to a lack of communication between the organization and the new employee, as well as the common nature of these environments where information is spread across job titles and sources. This thesis discusses the feasibility of implementing a basic chatbot that will allow new hires to ask questions and request varied information at all times, using an interface such as a messaging app. This research explores the way chatbots should be designed in order to be effective, reliable and enjoyable from a user experience perspective. The chatbot was developed using the Chatfuel platform and tested by new employees at a corporate environment. The users were requested to explore the chatbot freely and then asked to answer a survey. The interactions were also recorded and analyzed from in both qualitative and quantitative ways (chat logs and analytics). The study proves that an onboarding chatbot is a useful tool for new hires and can be used as a communication facilitator between the organization and the new hires during the first weeks of employment, and also after that, serving as an information source and a broadcasting method. The chatbot gives the new hires an accessible source of information that helps on the process of getting to speed, and enables a positive experience that increases familiarity in the new workplace.
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Kero, Chanelle, and Veronica Törnblom. "Utveckling av en chatbots dialog för implementation i en webbaserad kundtjänst." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-69046.

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Automatisering av kundtjänster genom implementering av chatbotar blir allt vanligare för att kunna erbjuda kunder en mer tillgänglig och effektiv service. Vid implementering av chatbotar i kundtjänster lämnas den mänskliga kontrollen och kundens upplevelse delvis över till ett digitalt system, vilket ställer krav på utformning av chatbotars dialoger. Syftet med arbetet är att ta fram en artefakt i form av en chatbot och utveckla dess dialog för en framtida implementation i en kundtjänst. Arbetet ämnar att ta fram designprinciper för utformning och utveckling av chatbotar och deras dialoger i en kundtjänstmiljö. Den forskningsmetod som tillämpades vid utvecklingen av artefakten var Design Science Research Methodology (DSRM). En litteraturstudie genomfördes och data samlades in genom möten samt en intervju med det företag som varit uppdragsgivare till arbetet. Arbetet resulterade i en demosida innehållande en chatbot, samt en sida för administratörer där de har en översikt av chatbotens genomförda interaktioner. Tre designprinciper togs även fram för utveckling av chatbotars dialog inom kundtjänst, vilka blev arbetets bidrag. De slutsatser som identifierades var att en chatbot bör kontrollera att användaren fått svar på sitt kundtjänstärende, en chatbot bör erbjuda mänsklig service vid missförstånd, en chatbot kan inte ersätta mänsklig service fullt ut samt att en chatbot bör ha en strukturerad datainsamling av genomförda interaktioner för att kunna utveckla och förbättra chatbotens dialog.<br>Automation of customer service through implementation of chatbots is becoming increasingly common in order to provide customers with a more accessible and efficient service. When implementing chatbots in customer services, human control and the customer experience are partially shifted to a digital system, which puts requirement on the chatbot’s dialogue. The purpose of this report is to develop an artifact for a client. The artifact is a web page containing a chatbot. The report aims to develop design principles for development of chatbots and their dialogues in a customer service. The research method used in the development of the artifact was Design Science Research Methodology (DSRM). A literature study was conducted, and data was collected through meetings and an interview with the client. The report resulted in a demo page containing a chatbot and a page for administrators with an overview of saved chats. Three design principles were also formulated for developing chatbots in customer service, which is the contribution of the report. The conclusions identified were that a chatbot should check that the user got the correct answer, a chatbot should offer human service in case of misunderstanding, a chatbot cannot fully replace human service and a chatbot should have a structured data collection of completed interactions for developing and improvement of the chatbot’s dialogue.
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Gligorijevic, Ilic Nemanja. "Utveckling av en FAQ chatbot - för frågor om ett program på ett universitet." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-85197.

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The implementation of chatbots and service services is becoming more common. The reason for this is that they are constantly available to answer questions, no matter what time it is. To make one chatbot is not just to write questions and specific answers. Communicating with one chatbot should, as much as possible, look like communicating with another human. The purpose of this report is to create a chatbot that will be used at Luleå university of technology and that will answer questions about system science. Furthermore, the goal is to investigate which design principles should be implemented, their possible concretization, when making chatbots, and possibly coming to new principles. The method used to make the chatbot is Design Science Research Methodology (DSRM). DSRM focuses on solving the problem by creating an IT artifact, which in this case is a chatbot. The result of this work is a created chatbot and design principles that were implemented during the development process.
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Lavista, Andrea. "Natural language processing: chatbot per gli studenti del Campus di Cesena." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19555/.

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Il natural language processing è un campo di ricerca interdisciplinare che spazia tra informatica, linguistica e intelligenza artificiale, che si pone come obiettivo il trattamento automatico del linguaggio naturale, ovvero l’analisi, la comprensione e l’elaborazione del linguaggio da parte dei calcolatori. Uno dei problemi più interessanti e ancora aperti di questa disciplina è la capacità di dialogo per la quale ricercatori e studiosi stanno ideando e impiegando nuovi approcci, in particolare basati su reti neurali artificiali, raggiungendo risultati sempre migliori. In questa tesi, oltre a dare una panoramica dello stato dell'arte, si espone lo sviluppo di un chatbot in contesto universitario, realizzato con il framework Rasa. Esso è pensato per gli studenti del Campus di Cesena e fornisce informazioni relative alla vita universitaria, principalmente sui corsi di laurea e sui bandi di Ateneo.
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Lipecki, Johan, and Viggo Lundén. "The Effect of Data Quantity on Dialog System Input Classification Models." Thesis, KTH, Hälsoinformatik och logistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-237282.

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This paper researches how different amounts of data affect different word vector models for classification of dialog system user input. A hypothesis is tested that there is a data threshold for dense vector models to reach the state-of-the-art performance that have been shown with recent research, and that character-level n-gram word-vector classifiers are especially suited for Swedish classifiers–because of compounding and the character-level n-gram model ability to vectorize out-of-vocabulary words. Also, a second hypothesis is put forward that models trained with single statements are more suitable for chat user input classification than models trained with full conversations. The results are not able to support neither of our hypotheses but show that sparse vector models perform very well on the binary classification tasks used. Further, the results show that 799,544 words of data is insufficient for training dense vector models but that training the models with full conversations is sufficient for single statement classification as the single-statement- trained models do not show any improvement in classifying single statements.<br>Detta arbete undersöker hur olika datamängder påverkar olika slags ordvektormodeller för klassificering av indata till dialogsystem. Hypotesen att det finns ett tröskelvärde för träningsdatamängden där täta ordvektormodeller när den högsta moderna utvecklingsnivån samt att n-gram-ordvektor-klassificerare med bokstavs-noggrannhet lämpar sig särskilt väl för svenska klassificerare söks bevisas med stöd i att sammansättningar är särskilt produktiva i svenskan och att bokstavs-noggrannhet i modellerna gör att tidigare osedda ord kan klassificeras. Dessutom utvärderas hypotesen att klassificerare som tränas med enkla påståenden är bättre lämpade att klassificera indata i chattkonversationer än klassificerare som tränats med hela chattkonversationer. Resultaten stödjer ingendera hypotes utan visar istället att glesa vektormodeller presterar väldigt väl i de genomförda klassificeringstesterna. Utöver detta visar resultaten att datamängden 799 544 ord inte räcker till för att träna täta ordvektormodeller väl men att konversationer räcker gott och väl för att träna modeller för klassificering av frågor och påståenden i chattkonversationer, detta eftersom de modeller som tränats med användarindata, påstående för påstående, snarare än hela chattkonversationer, inte resulterar i bättre klassificerare för chattpåståenden.
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Fornander, Linnea. "Språklig anpassning till en artificiell dialogpartner." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139246.

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Trots att antalet medier via vilka vi människor kan kommunicera med varandra är högre än någonsin, är talad interaktion något unikt betydelsefullt för våra mänskliga samhällen och relationer och därför ett ständigt relevant forskningsområde. Denna studie undersökte kvantitativa skillnader i hur människor konverserar med en talbaserad chattbot jämfört med en mänsklig konversationspartner, då de utförde en uppgift utformad för att skapa jämförliga dialoger. Resultatet visade på att konversationerna med chattbotten var mindre effektiva och ledde till sämre prestation i uppgiften. Dessutom påvisades att deltagarna använde en kortare genomsnittlig turlängd i konversationer med chattbotten, samt signifikanta skillnader i ordvariation mellan de två betingelserna. Dessa skillnader kan bero på att människor anpassar sitt språk efter sina uppfattningar av chattbottens kommunikativa egenskaper, och innebär att möjligheten att generalisera egenskaper hos människors tal i konversationer med liknande artificiella dialogpartners till konversationer med människor kan vara begränsad.
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Books on the topic "Chatbot dialogue"

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McTear, Michael. Conversational AI: Dialogue Systems, Conversational Agents, and Chatbots. Springer International Publishing AG, 2020.

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McTear, Michael. Conversational AI: Dialogue Systems, Conversational Agents, and Chatbots. Morgan & Claypool Publishers, 2020.

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McTear, Michael. Conversational AI: Dialogue Systems, Conversational Agents, and Chatbots. Morgan & Claypool Publishers, 2020.

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McTear, Michael. Conversational AI: Dialogue Systems, Conversational Agents, and Chatbots. Morgan & Claypool Publishers, 2020.

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Galley, Michel, Jianfeng Gao, and Lihong Li. Neural Approaches to Conversational AI: Question Answering, Task-Oriented Dialogues and Social Chatbots. Now Publishers, 2019.

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Beaven, Tita, and Fernando Rosell-Aguilar, eds. Innovative language pedagogy report. Research-publishing.net, 2021. http://dx.doi.org/10.14705/rpnet.2021.50.9782490057863.

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The Innovative Language Pedagogy Report presents new and emerging approaches to language teaching, learning, and assessment in school, further education, and higher education settings. Researchers and practitioners provide 22 research-informed, short articles on their chosen pedagogy, with examples and resources. The report is jargon-free, written in a readable format, and covers, among others, gamification, open badges, comparative judgement, translanguaging, translation, learning without a teacher, and dialogue facilitation. It also includes technologies such as chatbots, augmented reality, automatic speech recognition, digital corpora, and LMOOCs, as well as pedagogical innovations around virtual exchange, digital storytelling, technology-facilitated oral homework, and TeachMeets.
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Book chapters on the topic "Chatbot dialogue"

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Lobo, Inês, Diogo Rato, Rui Prada, and Frank Dignum. "Socially Aware Interactions: From Dialogue Trees to Natural Language Dialogue Systems." In Chatbot Research and Design. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94890-0_8.

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Yildiz, Eren, Suna Bensch, and Frank Dignum. "Incorporating Social Practices in Dialogue Systems." In Chatbot Research and Design. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94890-0_7.

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Tegos, Stergios, Stavros Demetriadis, Georgios Psathas, and Thrasyvoulos Tsiatsos. "A Configurable Agent to Advance Peers’ Productive Dialogue in MOOCs." In Chatbot Research and Design. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39540-7_17.

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Fergencs, Tamás, and Florian Meier. "Engagement and Usability of Conversational Search – A Study of a Medical Resource Center Chatbot." In Diversity, Divergence, Dialogue. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71292-1_26.

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Yuwono, Steven Kester, Biao Wu, and Luis Fernando D’Haro. "Automated Scoring of Chatbot Responses in Conversational Dialogue." In Lecture Notes in Electrical Engineering. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9443-0_31.

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Assayed, Suha Khalil, Manar Alkhatib, and Khaled Shaalan. "The Key Challenges in Educational Advising Chatbot Dialogue System." In Innovation in the University 4.0 System based on Smart Technologies. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003425809-5.

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Callejas-Rodríguez, Ángel, Esaú Villatoro-Tello, Ivan Meza, and Gabriela Ramírez-de-la-Rosa. "From Dialogue Corpora to Dialogue Systems: Generating a Chatbot with Teenager Personality for Preventing Cyber-Pedophilia." In Text, Speech, and Dialogue. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45510-5_61.

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Matsuura, Shu, and Riki Ishimura. "Chatbot and Dialogue Demonstration with a Humanoid Robot in the Lecture Class." In Universal Access in Human–Computer Interaction. Human and Technological Environments. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58700-4_20.

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Li, Junmei. "Multi-round Dialogue Intention Recognition Method for a Chatbot Baed on Deep Learning." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18123-8_44.

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Galitsky, Boris. "Discourse-Level Dialogue Management." In Developing Enterprise Chatbots. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04299-8_11.

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Conference papers on the topic "Chatbot dialogue"

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Ismail, Jabri, Aboulbichr Ahmed, and El ouaazizi Aziza. "Improving a Sequence-to-sequence NLP Model using a Reinforcement Learning Policy Algorithm." In 12th International Conference on Artificial Intelligence, Soft Computing and Applications. Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.122317.

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Nowadays, the current neural network models of dialogue generation(chatbots) show great promise for generating answers for chatty agents. But they are short-sighted in that they predict utterances one at a time while disregarding their impact on future outcomes. Modelling a dialogue’s future direction is critical for generating coherent, interesting dialogues, a need that has led traditional NLP dialogue models that rely on reinforcement learning. In this article, we explain how to combine these objectives by using deep reinforcement learning to predict future rewards in chatbot dialogue. The model simulates conversations between two virtual agents, with policy gradient methods used to reward sequences that exhibit three useful conversational characteristics: the flow of informality, coherence, and simplicity of response (related to forward-looking function). We assess our model based on its diversity, length, and complexity with regard to humans. In dialogue simulation, evaluations demonstrated that the proposed model generates more interactive responses and encourages a more sustained successful conversation. This work commemorates a preliminary step toward developing a neural conversational model based on the long-term success of dialogues.
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Galitsky, Boris, Dmitry Ilvovsky, and Elizaveta Goncharova. "On a Chatbot Conducting Dialogue-in-Dialogue." In Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue. Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/w19-5916.

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Raimer, Stephan, and Marleen Vanhauer. "Heuristic Evaluation of Public Service Chatbots." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001712.

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In recent years, chatbots have been adopted in business contexts and also for public services at a growing rate. Chatbots provide dialogue interfaces combining visual elements with natural conversation. Good Conversational Design in this context covers the topics of Natural-Language Processing (NLP) and Dialogue Management (DM). Few attention has been paid to the usability evaluation of conversational interfaces (Höhn &amp; Bongard-Blanchy, 2021). The present paper builds upon the work by Höhn &amp; Bongard-Blanchy by applying their framework of conversational heuristics to evaluate a set of public service chatbots operated in the federal state of Schleswig-Holstein. Thus, for each public service chatbot, a usability score is established and typical characteristics of public service chatbots in general are summarized. We discuss results by comparing the overall scores, weaknesses and strengths of each chatbot. In addition, we reflect on our experience in the application of the framework as well as highlight possible optimization potentials. Concludingly, this paper collects UX recommendations for public service chatbots.
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Paula, Robson T., Décio G. Aguiar Neto, Davi Romero, and Paulo T. Guerra. "Evaluation of Synthetic Datasets Generation for Intent Classification Tasks in Portuguese." In Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana. Sociedade Brasileira de Computação, 2021. http://dx.doi.org/10.5753/stil.2021.17806.

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A chatbot is an artificial intelligence based system aimed at chatting with users, commonly used as a virtual assistant to help people or answer questions. Intent classification is an essential task for chatbots where it aims to identify what the user wants in a certain dialogue. However, for many domains, little data are available to properly train those systems. In this work, we evaluate the performance of two methods to generate synthetic data for chatbots, one based on template questions and another based on neural text generation. We build four datasets that are used training chatbot components in the intent classification task. We intend to simulate the task of migrating a search-based portal to an interactive dialogue-based information service by using artificial datasets for initial model training. Our results show that template-based datasets are slightly superior to those neural-based generated in our application domain, however, neural-generated present good results and they are a viable option when one has limited access to domain experts to hand-code text templates.
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Hancock, Braden, Antoine Bordes, Pierre-Emmanuel Mazare, and Jason Weston. "Learning from Dialogue after Deployment: Feed Yourself, Chatbot!" In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/p19-1358.

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Galitsky, Boris, and Dmitry Ilvovsky. "Chatbot with a Discourse Structure-Driven Dialogue Management." In Proceedings of the Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 2017. http://dx.doi.org/10.18653/v1/e17-3022.

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Kao, Chien-Hao, Chih-Chieh Chen, and Yu-Tza Tsai. "Model of Multi-turn Dialogue in Emotional Chatbot." In 2019 International Conference on Technologies and Applications of Artificial Intelligence (TAAI). IEEE, 2019. http://dx.doi.org/10.1109/taai48200.2019.8959855.

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Ilievski, Vladimir, Claudiu Musat, Andreea Hossman, and Michael Baeriswyl. "Goal-Oriented Chatbot Dialog Management Bootstrapping with Transfer Learning." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/572.

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Goal-Oriented (GO) Dialogue Systems, colloquially known as goal oriented chatbots, help users achieve a predefined goal (e.g. book a movie ticket) within a closed domain. A first step is to understand the user's goal by using natural language understanding techniques. Once the goal is known, the bot must manage a dialogue to achieve that goal, which is conducted with respect to a learnt policy. The success of the dialogue system depends on the quality of the policy, which is in turn reliant on the availability of high-quality training data for the policy learning method, for instance Deep Reinforcement Learning. Due to the domain specificity, the amount of available data is typically too low to allow the training of good dialogue policies. In this paper we introduce a transfer learning method to mitigate the effects of the low in-domain data availability. Our transfer learning based approach improves the bot's success rate by 20% in relative terms for distant domains and we more than double it for close domains, compared to the model without transfer learning. Moreover, the transfer learning chatbots learn the policy up to 5 to 10 times faster. Finally, as the transfer learning approach is complementary to additional processing such as warm-starting, we show that their joint application gives the best outcomes.
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Wu, Shih-Hung, Liang-Pu Chen, Ping-Che Yang, and Tsun Ku. "Automatic Dialogue Template Synthesis for Chatbot by Story Information Extraction." In 2018 IEEE International Conference on Information Reuse and Integration for Data Science (IRI). IEEE, 2018. http://dx.doi.org/10.1109/iri.2018.00077.

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Lee, Seugnjun, Yoonna Jang, Chanjun Park, et al. "PEEP-Talk: A Situational Dialogue-based Chatbot for English Education." In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations). Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.acl-demo.18.

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