Littérature scientifique sur le sujet « Chatbot dialogue »
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Articles de revues sur le sujet "Chatbot dialogue"
Fang, Jiyang. « Analysis on Chatbot Performance based on Attention Mechanism ». Highlights in Science, Engineering and Technology 39 (1 avril 2023) : 151–56. http://dx.doi.org/10.54097/hset.v39i.6517.
Texte intégralSagstad, Mari Haaland, Nils-Halvdan Morken, Agnethe Lund, Linn Jannike Dingsør, Anne Britt Vika Nilsen et Linn Marie Sorbye. « Quantitative User Data From a Chatbot Developed for Women With Gestational Diabetes Mellitus : Observational Study ». JMIR Formative Research 6, no 4 (18 avril 2022) : e28091. http://dx.doi.org/10.2196/28091.
Texte intégralTsai, Wan-Hsiu Sunny, Yu Liu et 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 (18 juin 2021) : 460–82. http://dx.doi.org/10.1108/jrim-12-2019-0200.
Texte intégralLin, Chien-Chang, Anna Y. Q. Huang et Stephen J. H. Yang. « A Review of AI-Driven Conversational Chatbots Implementation Methodologies and Challenges (1999–2022) ». Sustainability 15, no 5 (22 février 2023) : 4012. http://dx.doi.org/10.3390/su15054012.
Texte intégralGregorcic, Bor, et Ann-Marie Pendrill. « ChatGPT and the frustrated Socrates ». Physics Education 58, no 3 (22 mars 2023) : 035021. http://dx.doi.org/10.1088/1361-6552/acc299.
Texte intégralKumar, Kartik. « An Educational Chatbot Using AI in Radiotherapy ». INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no 05 (16 mai 2024) : 1–5. http://dx.doi.org/10.55041/ijsrem34122.
Texte intégralLi, Jingquan. « Security Implications of AI Chatbots in Health Care ». Journal of Medical Internet Research 25 (28 novembre 2023) : e47551. http://dx.doi.org/10.2196/47551.
Texte intégralČerný, 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 (28 janvier 2023) : 284–305. http://dx.doi.org/10.3390/ejihpe13020022.
Texte intégralShawar, Bayan Abu, et Eric Steven Atwell. « Using corpora in machine-learning chatbot systems ». International Journal of Corpus Linguistics 10, no 4 (7 novembre 2005) : 489–516. http://dx.doi.org/10.1075/ijcl.10.4.06sha.
Texte intégralBodapati, Mrs Nagaeswari. « Campus Companion : Creating a Supportive Chat – Assistant for Students ». INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no 04 (2 avril 2024) : 1–5. http://dx.doi.org/10.55041/ijsrem29939.
Texte intégralThèses sur le sujet "Chatbot dialogue"
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.
Texte intégralBouguelia, 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.
Texte intégralDialogue 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
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.
Texte intégralThis 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%
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.
Texte intégralAsher, 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.
Texte intégralKero, Chanelle, et 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.
Texte intégralAutomation 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.
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.
Texte intégralLavista, 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/.
Texte intégralLipecki, Johan, et 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.
Texte intégralDetta 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.
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.
Texte intégralLivres sur le sujet "Chatbot dialogue"
McTear, Michael. Conversational AI : Dialogue Systems, Conversational Agents, and Chatbots. Springer International Publishing AG, 2020.
Trouver le texte intégralMcTear, Michael. Conversational AI : Dialogue Systems, Conversational Agents, and Chatbots. Morgan & Claypool Publishers, 2020.
Trouver le texte intégralMcTear, Michael. Conversational AI : Dialogue Systems, Conversational Agents, and Chatbots. Morgan & Claypool Publishers, 2020.
Trouver le texte intégralMcTear, Michael. Conversational AI : Dialogue Systems, Conversational Agents, and Chatbots. Morgan & Claypool Publishers, 2020.
Trouver le texte intégralGalley, Michel, Jianfeng Gao et Lihong Li. Neural Approaches to Conversational AI : Question Answering, Task-Oriented Dialogues and Social Chatbots. Now Publishers, 2019.
Trouver le texte intégralBeaven, Tita, et Fernando Rosell-Aguilar, dir. Innovative language pedagogy report. Research-publishing.net, 2021. http://dx.doi.org/10.14705/rpnet.2021.50.9782490057863.
Texte intégralChapitres de livres sur le sujet "Chatbot dialogue"
Lobo, Inês, Diogo Rato, Rui Prada et Frank Dignum. « Socially Aware Interactions : From Dialogue Trees to Natural Language Dialogue Systems ». Dans Chatbot Research and Design, 124–40. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94890-0_8.
Texte intégralYildiz, Eren, Suna Bensch et Frank Dignum. « Incorporating Social Practices in Dialogue Systems ». Dans Chatbot Research and Design, 108–23. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94890-0_7.
Texte intégralTegos, Stergios, Stavros Demetriadis, Georgios Psathas et Thrasyvoulos Tsiatsos. « A Configurable Agent to Advance Peers’ Productive Dialogue in MOOCs ». Dans Chatbot Research and Design, 245–59. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39540-7_17.
Texte intégralFergencs, Tamás, et Florian Meier. « Engagement and Usability of Conversational Search – A Study of a Medical Resource Center Chatbot ». Dans Diversity, Divergence, Dialogue, 328–45. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71292-1_26.
Texte intégralYuwono, Steven Kester, Biao Wu et Luis Fernando D’Haro. « Automated Scoring of Chatbot Responses in Conversational Dialogue ». Dans Lecture Notes in Electrical Engineering, 357–69. Singapore : Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9443-0_31.
Texte intégralAssayed, Suha Khalil, Manar Alkhatib et Khaled Shaalan. « The Key Challenges in Educational Advising Chatbot Dialogue System ». Dans Innovation in the University 4.0 System based on Smart Technologies, 74–82. Boca Raton : Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003425809-5.
Texte intégralCallejas-Rodríguez, Ángel, Esaú Villatoro-Tello, Ivan Meza et Gabriela Ramírez-de-la-Rosa. « From Dialogue Corpora to Dialogue Systems : Generating a Chatbot with Teenager Personality for Preventing Cyber-Pedophilia ». Dans Text, Speech, and Dialogue, 531–39. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45510-5_61.
Texte intégralMatsuura, Shu, et Riki Ishimura. « Chatbot and Dialogue Demonstration with a Humanoid Robot in the Lecture Class ». Dans Universal Access in Human–Computer Interaction. Human and Technological Environments, 233–46. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58700-4_20.
Texte intégralLi, Junmei. « Multi-round Dialogue Intention Recognition Method for a Chatbot Baed on Deep Learning ». Dans Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 561–72. Cham : Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18123-8_44.
Texte intégralGalitsky, Boris. « Discourse-Level Dialogue Management ». Dans Developing Enterprise Chatbots, 365–426. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04299-8_11.
Texte intégralActes de conférences sur le sujet "Chatbot dialogue"
Ismail, Jabri, Aboulbichr Ahmed et El ouaazizi Aziza. « Improving a Sequence-to-sequence NLP Model using a Reinforcement Learning Policy Algorithm ». Dans 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.
Texte intégralGalitsky, Boris, Dmitry Ilvovsky et Elizaveta Goncharova. « On a Chatbot Conducting Dialogue-in-Dialogue ». Dans Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue. Stroudsburg, PA, USA : Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/w19-5916.
Texte intégralRaimer, Stephan, et Marleen Vanhauer. « Heuristic Evaluation of Public Service Chatbots ». Dans 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001712.
Texte intégralPaula, Robson T., Décio G. Aguiar Neto, Davi Romero et Paulo T. Guerra. « Evaluation of Synthetic Datasets Generation for Intent Classification Tasks in Portuguese ». Dans Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana. Sociedade Brasileira de Computação, 2021. http://dx.doi.org/10.5753/stil.2021.17806.
Texte intégralHancock, Braden, Antoine Bordes, Pierre-Emmanuel Mazare et Jason Weston. « Learning from Dialogue after Deployment : Feed Yourself, Chatbot ! » Dans Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA, USA : Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/p19-1358.
Texte intégralGalitsky, Boris, et Dmitry Ilvovsky. « Chatbot with a Discourse Structure-Driven Dialogue Management ». Dans Proceedings of the Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics. Stroudsburg, PA, USA : Association for Computational Linguistics, 2017. http://dx.doi.org/10.18653/v1/e17-3022.
Texte intégralKao, Chien-Hao, Chih-Chieh Chen et Yu-Tza Tsai. « Model of Multi-turn Dialogue in Emotional Chatbot ». Dans 2019 International Conference on Technologies and Applications of Artificial Intelligence (TAAI). IEEE, 2019. http://dx.doi.org/10.1109/taai48200.2019.8959855.
Texte intégralIlievski, Vladimir, Claudiu Musat, Andreea Hossman et Michael Baeriswyl. « Goal-Oriented Chatbot Dialog Management Bootstrapping with Transfer Learning ». Dans Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California : International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/572.
Texte intégralWu, Shih-Hung, Liang-Pu Chen, Ping-Che Yang et Tsun Ku. « Automatic Dialogue Template Synthesis for Chatbot by Story Information Extraction ». Dans 2018 IEEE International Conference on Information Reuse and Integration for Data Science (IRI). IEEE, 2018. http://dx.doi.org/10.1109/iri.2018.00077.
Texte intégralLee, Seugnjun, Yoonna Jang, Chanjun Park, Jungseob Lee, Jaehyung Seo, Hyeonseok Moon, Sugyeong Eo, Seounghoon Lee, Bernardo Yahya et Heuiseok Lim. « PEEP-Talk : A Situational Dialogue-based Chatbot for English Education ». Dans Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3 : System Demonstrations). Stroudsburg, PA, USA : Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.acl-demo.18.
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