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Статті в журналах з теми "Assistant conversationnel"

Автор: Grafiati

Опубліковано: 7 липня 2024

Оновлено: 7 липня 2024

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1

xuetao, Mao, Jean-Paul Sansonnet, and François Bouchet. "Définition d’un agent conversationnel assistant d’applications internet à partir d’un corpus de requêtes." Techniques et sciences informatiques 29, no. 10 (2010): 1123–54. http://dx.doi.org/10.3166/tsi.29.1123-1154.

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2

Bickmore, Timothy W., Stefán Ólafsson, and Teresa K. O'Leary. "Mitigating Patient and Consumer Safety Risks When Using Conversational Assistants for Medical Information: Exploratory Mixed Methods Experiment." Journal of Medical Internet Research 23, no. 11 (2021): e30704. http://dx.doi.org/10.2196/30704.

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Background Prior studies have demonstrated the safety risks when patients and consumers use conversational assistants such as Apple’s Siri and Amazon’s Alexa for obtaining medical information. Objective The aim of this study is to evaluate two approaches to reducing the likelihood that patients or consumers will act on the potentially harmful medical information they receive from conversational assistants. Methods Participants were given medical problems to pose to conversational assistants that had been previously demonstrated to result in potentially harmful recommendations. Each conversatio

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3

L, Manigandan, and Sivakumar Alur. "Mapping the Research Landscape of Chatbots, Conversational Agents, and Virtual Assistants in Business, Management, and Accounting: A Bibliometric Review." Qubahan Academic Journal 3, no. 4 (2023): 502–13. http://dx.doi.org/10.58429/qaj.v3n4a252.

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This bibliometric review aims to map the research landscape of chatbots, conversational agents, and virtual assistants in the business, management, and accounting of the subject area. The review begins by examining the growth of research in this area over time, revealing an increasing interest in the application of chatbots, conversational agents, and virtual assistants in business, management, and accounting contexts. This study aims to contribute to the field of chatbot research by conducting a bibliometric analysis of chatbot, conversational agent and virtual assistant papers available in t

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4

Ansari, Tarique. "Conversational AI Assistant." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (2022): 1169–72. http://dx.doi.org/10.22214/ijraset.2022.47554.

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Abstract: A conversational assistant is an intelligent conversational computing system designed to mimic human speech to provide automated online guidance and support. The growing benefits of conversational support have led to widespread adoption across many industries to provide virtual support to their customers. Conversation assistance uses methods and algorithms from his two fields of artificial intelligence: natural language processing and machine learning. However, the application has many challenges and limitations. This research reviews recent advances in conversation support using art

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5

Geetha, Dr V., Dr C. K. Gomathy*, Mr Kottamasu Manasa Sri Vardhan, and Mr Nukala Pavan Kumar. "The Voice Enabled Personal Assistant for Pc using Python." International Journal of Engineering and Advanced Technology 10, no. 4 (2021): 162–65. http://dx.doi.org/10.35940/ijeat.d2425.0410421.

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Personal Assistants, or conversational interfaces, or chat bots reinvent a new way for individuals to interact with computes. A Personal Virtual Assistant allows a user to simply ask questions in the same manner that they would address a human, and are even capable of doing some basic tasks like opening apps, reading out news, taking notes etc., with just a voice command. Personal Assistants like Google Assistant, Alexa, Siri wo

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6

Khandave, Vrushali. "HOMMIE: CONVERSATIONAL AI ASSISTANT." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30718.

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Conversational AI assistants have witnessed remarkable progress in recent years, transforming human-computer interaction across a spectrum of applications. This paper offers a comprehensive overview of the state-of-the-art techniques, methodologies, and challenges in the field. We examine the core components of conversational AI, including natural language understanding, dialogue management, and response generation. Additionally, we address key challenges such as context modeling, personalization, and ethical considerations. The paper serves as a roadmap for researchers and developers, highlig

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7

Minh, Nguyen Thi Hong, and Le Duy Khanh. "Using Artificial Intelligence Tools for Developing Conversational Skills for English Majors." European Modern Studies Journal 8, no. 2 (2024): 461–69. http://dx.doi.org/10.59573/emsj.8(2).2024.40.

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Despite the potential of Virtual Conversation Assistants as effective tools for conversing support in developing students’ conversational skills, their presence in Vietnamese schools or universities remains limited, often due to cost constraints, quality of language interaction, and their unpopularity among Vietnamese students. Therefore, it is essential that English language educators seek alternative strategies to offer additional assistance for the enhancement of students' abilities to converse effectively. This experimental study pilots the implementation of Call Annie and Praktika, two ar

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8

Ortiz, Charles L. "Holistic Conversational Assistants." AI Magazine 39, no. 1 (2018): 88–90. http://dx.doi.org/10.1609/aimag.v39i1.2771.

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This column describes work being done at Nuance Communication in developing virtual personal assistants (VPAs) that can engage in extended task center dialogues and the involve the coordination of many complex modules, along with conversational and collaborative support to such VPAs.

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9

Pappula, Sharmila Reddy, and Sathwik Rao Allam. "LLMs for Conversational AI: Enhancing Chatbots and Virtual Assistants." International Journal of Research Publication and Reviews 4, no. 12 (2023): 1601–11. http://dx.doi.org/10.55248/gengpi.4.1223.123425.

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10

Parikh, Soham, Quaizar Vohra, and Mitul Tiwari. "Automated Utterance Generation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 08 (2020): 13344–49. http://dx.doi.org/10.1609/aaai.v34i08.7047.

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Conversational AI assistants are becoming popular and question-answering is an important part of any conversational assistant. Using relevant utterances as features in question-answering has shown to improve both the precision and recall for retrieving the right answer by a conversational assistant. Hence, utterance generation has become an important problem with the goal of generating relevant utterances (sentences or phrases) from a knowledge base article that consists of a title and a description. However, generating good utterances usually requires a lot of manual effort, creating the need

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11

Raees, Shaikh Areeba. "CHAT GPT ASSISTANT." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30218.

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The capacity of ChatGPT, a cutting-edge conversational AI model, to produce text responses that resemble those of a human being in a range of conversational contexts has attracted a lot of attention. By using extensive pretraining on a variety of textual data, ChatGPT generates discourse with impressive fluency and coherence, opening up new possibilities for improving human-computer interaction. In this study, we give a thorough analysis of ChatGPT's capabilities and constraints, examining how well it generates interesting and natural-sounding discussions. We explore several facets of ChatGPT'

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12

Seymour, William, and Max Van Kleek. "Exploring Interactions Between Trust, Anthropomorphism, and Relationship Development in Voice Assistants." Proceedings of the ACM on Human-Computer Interaction 5, CSCW2 (2021): 1–16. http://dx.doi.org/10.1145/3479515.

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Modern conversational agents such as Alexa and Google Assistant represent significant progress in speech recognition, natural language processing, and speech synthesis. But as these agents have grown more realistic, concerns have been raised over how their social nature might unconsciously shape our interactions with them. Through a survey of 500 voice assistant users, we explore whether users' relationships with their voice assistants can be quantified using the same metrics as social, interpersonal relationships; as well as if this correlates with how much they trust their devices and the ex

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13

Huang, Ting-Hao, Walter Lasecki, Amos Azaria, and Jeffrey Bigham. ""Is There Anything Else I Can Help You With?" Challenges in Deploying an On-Demand Crowd-Powered Conversational Agent." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 4 (September 21, 2016): 79–88. http://dx.doi.org/10.1609/hcomp.v4i1.13292.

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Intelligent conversational assistants, such as Apple's Siri, Microsoft's Cortana, and Amazon's Echo, have quickly become a part of our digital life. However, these assistants have major limitations, which prevents users from conversing with them as they would with human dialog partners. This limits our ability to observe how users really want to interact with the underlying system. To address this problem, we developed a crowd-powered conversational assistant, Chorus, and deployed it to see how users and workers would interact together when mediated by the system. Chorus sophisticatedly conver

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14

Krommyda, Maria, and Verena Kantere. "Semantic Analysis for Conversational Datasets: Improving Their Quality Using Semantic Relationships." International Journal of Semantic Computing 14, no. 03 (2020): 395–422. http://dx.doi.org/10.1142/s1793351x2050004x.

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As more and more datasets become available, their utilization in different applications increases in popularity. Their volume and production rate, however, means that their quality and content control is in most cases non-existing, resulting in many datasets that contain inaccurate information of low quality. Especially, in the field of conversational assistants, where the datasets come from many heterogeneous sources with no quality assurance, the problem is aggravated. We present here an integrated platform that creates task- and topic-specific conversational datasets to be used for training

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15

Acer, Utku Günay, Marc van den Broeck, Chulhong Min, Mallesham Dasari, and Fahim Kawsar. "The City as a Personal Assistant." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, no. 2 (2022): 1–31. http://dx.doi.org/10.1145/3534573.

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Conversational agents are increasingly becoming digital partners in our everyday computational experiences. Although rich and fresh in content, they are oblivious to users' locality beyond geospatial weather and traffic conditions. We introduce Lingo, a hyper-local conversational agent embedded deeply into the urban infrastructure that provides rich, purposeful, detailed, and in some cases, playful information relevant to a neighbourhood. Drawing lessons from a mixed-method contextual study (online survey, n = 1992 and semi-structured interviews, n = 21), we identify requirements for such a hy

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16

Bajaj, Divij, and Dhanya Pramod. "Conversational System, Intelligent Virtual Assistant (IVA) Named DIVA Using Raspberry Pi." International Journal of Security and Privacy in Pervasive Computing 12, no. 4 (2020): 38–52. http://dx.doi.org/10.4018/ijsppc.2020100104.

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Humans are living in an era where they are interacting with machines day in and day out. In this new era of the 21st century, a virtual assistant (IVA) is a boon for everyone. It has opened the way for a new world where devices can interact their own. The human voice is integrated with every device making it intelligent. These IVAs can also be used to integrate it with business intelligence software such as Tableau and PowerBI to give dashboards the power of voice and text insights using NLG (natural language generation). This new technology attracted almost the entire world like smart phones,

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17

Cebrián, Javier, Ramón Martínez-Jiménez, Natalia Rodriguez, and Luis Fernando D’Haro. "Considerations on Creating Conversational Agents For Multiple Environments and Users." AI Magazine 42, no. 2 (2021): 71–86. http://dx.doi.org/10.1609/aimag.v42i2.7484.

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Advances in artificial intelligence algorithms and expansion of straightforward cloud-based platforms have enabled the adoption of conversational assistants by both, medium and large companies, to facilitate interaction between clients and employees. The interactions are possible through the use of ubiquitous devices (e.g., Amazon Echo, Apple HomePod, Google Nest), virtual assistants (e.g., Apple Siri, Google Assistant, Samsung Bixby, or Microsoft Cortana), chat windows on the corporate website, or social network applications (e.g. Facebook Messenger, Telegram, Slack, WeChat).Creating a useful

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18

Pérez, Anxo, Paula Lopez-Otero, and Javier Parapar. "Designing an Open Source Virtual Assistant." Proceedings 54, no. 1 (2020): 30. http://dx.doi.org/10.3390/proceedings2020054030.

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A chatbot is a type of agent that allows people to interact with an information repository using natural language. Nowadays, chatbots have been incorporated in the form of conversational assistants on the most important mobile and desktop platforms. In this article, we present our design of an assistant developed with open-source and widely used components. Our proposal covers the process end-to-end, from information gathering and processing to visual and speech-based interaction. We have deployed a proof of concept over the website of our Computer Science Faculty.

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19

Brinkschulte, Luisa, Stephan Schlögl, Alexander Monz, Pascal Schöttle, and Matthias Janetschek. "Perspectives on Socially Intelligent Conversational Agents." Multimodal Technologies and Interaction 6, no. 8 (2022): 62. http://dx.doi.org/10.3390/mti6080062.

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The propagation of digital assistants is consistently progressing. Manifested by an uptake of ever more human-like conversational abilities, respective technologies are moving increasingly away from their role as voice-operated task enablers and becoming rather companion-like artifacts whose interaction style is rooted in anthropomorphic behavior. One of the required characteristics in this shift from a utilitarian tool to an emotional character is the adoption of social intelligence. Although past research has recognized this need, more multi-disciplinary investigations should be devoted to t

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20

Shrivastava, Pragya, and G. Bharadwaja Kumar. "WORKING OF ACONTEXT-AWARE CONVERSATIONAL ENTITY." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (2017): 202. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19638.

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Abstract — Introduction of new technologies in to the world is increasing rapidly and in order to assist the users to get equipped with such technologies industries are providing customer care services. Contacting a customer care service is subjective to several overheads of selecting options from a listed set, waiting for the switching between selections and awaiting the support of a customer care executive as the process usually requires a human intervention. Hence, a substitute for a personnel is required by the IT industries in order to automate the communication process in assisting the c

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21

Davis, Courtney R., Karen J. Murphy, Rachel G. Curtis, and Carol A. Maher. "A Process Evaluation Examining the Performance, Adherence, and Acceptability of a Physical Activity and Diet Artificial Intelligence Virtual Health Assistant." International Journal of Environmental Research and Public Health 17, no. 23 (2020): 9137. http://dx.doi.org/10.3390/ijerph17239137.

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Artificial intelligence virtual health assistants are a promising emerging technology. This study is a process evaluation of a 12-week pilot physical activity and diet program delivered by virtual assistant “Paola”. This single-arm repeated measures study (n = 28, aged 45–75 years) was evaluated on technical performance (accuracy of conversational exchanges), engagement (number of weekly check-ins completed), adherence (percentage of step goal and recommended food servings), and user feedback. Paola correctly asked scripted questions and responded to participants during the check-ins 97% and 9

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22

Bouchet, François, and Jean-Paul Sansonnet. "Agents conversationnels psychologiques. Modélisation des réactions rationnelles et comportementales des agents assistants conversationnels." Revue d'intelligence artificielle 27, no. 6 (2013): 679–708. http://dx.doi.org/10.3166/ria.27.679-708.

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23

Rastogi, Abhinav, Xiaoxue Zang, Srinivas Sunkara, Raghav Gupta, and Pranav Khaitan. "Towards Scalable Multi-Domain Conversational Agents: The Schema-Guided Dialogue Dataset." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 8689–96. http://dx.doi.org/10.1609/aaai.v34i05.6394.

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Virtual assistants such as Google Assistant, Alexa and Siri provide a conversational interface to a large number of services and APIs spanning multiple domains. Such systems need to support an ever-increasing number of services with possibly overlapping functionality. Furthermore, some of these services have little to no training data available. Existing public datasets for task-oriented dialogue do not sufficiently capture these challenges since they cover few domains and assume a single static ontology per domain. In this work, we introduce the the Schema-Guided Dialogue (SGD) dataset, conta

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24

Hwang, Inseok, Youngki Lee, Chungkuk Yoo, Chulhong Min, Dongsun Yim, and John Kim. "Towards Interpersonal Assistants: Next-Generation Conversational Agents." IEEE Pervasive Computing 18, no. 2 (2019): 21–31. http://dx.doi.org/10.1109/mprv.2019.2922907.

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25

Mohammed, Dr Farheen. "Conversational AI: Designing Intelligent Chatbots and Virtual Assistants." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34774.

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Conversational AI has revolutionized the way humans interact with machines. By leveraging advances in Natural Language Processing (NLP) and Machine Learning (ML), intelligent Chatbots and virtual assistants have become integral tools across various sectors. This paper explores the design principles, technological frameworks, and application areas of conversational AI, focusing on the challenges and future directions of this rapidly evolving field.

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26

Mekni, Mehdi. "An Artificial Intelligence Based Virtual Assistant Using Conversational Agents." Journal of Software Engineering and Applications 14, no. 09 (2021): 455–73. http://dx.doi.org/10.4236/jsea.2021.149027.

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27

Kim, Kang-Min, Woo-Jong Ryu, Jun-Hyung Park, and SangKeun Lee. "meChat: In-Device Personal Assistant for Conversational Photo Sharing." IEEE Internet Computing 23, no. 2 (2019): 23–30. http://dx.doi.org/10.1109/mic.2018.2883059.

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28

Bouchet, François, and Jean-Paul Sansonnet. "Agents conversationnels psychologiques. Un cadre d'étude des comportements rationnels et psychologiques des agents assistants conversationnels." Revue d'intelligence artificielle 25, no. 5 (2011): 591–623. http://dx.doi.org/10.3166/ria.25.591-623.

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29

Fontecha, Jesús, Iván González, and Alberto Salas-Seguín. "Using Conversational Assistants and Connected Devices to Promote a Responsible Energy Consumption at Home." Proceedings 31, no. 1 (2019): 32. http://dx.doi.org/10.3390/proceedings2019031032.

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Today, households worldwide are being increasingly connected. Mobile devices and embedded systems carry out many tasks supported by applications which are based on artificial intelligence algorithms with the aim of leading homes to be smarter. One of the purposes of these systems is to connect appliances to the power network, as well as to the internet to monitor consumption data among others. In addition, new interaction ways are emerging to manage all these systems. For example, conversational assistants which allow us to interact by voice with devices at home. In this work, we present Green

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30

Curtis, Rachel G., Bethany Bartel, Ty Ferguson, et al. "Improving User Experience of Virtual Health Assistants: Scoping Review." Journal of Medical Internet Research 23, no. 12 (2021): e31737. http://dx.doi.org/10.2196/31737.

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Background Virtual assistants can be used to deliver innovative health programs that provide appealing, personalized, and convenient health advice and support at scale and low cost. Design characteristics that influence the look and feel of the virtual assistant, such as visual appearance or language features, may significantly influence users’ experience and engagement with the assistant. Objective This scoping review aims to provide an overview of the experimental research examining how design characteristics of virtual health assistants affect user experience, summarize research findings of

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31

Kraus, Matthias, Nicolas Wagner, Zoraida Callejas, and Wolfgang Minker. "The Role of Trust in Proactive Conversational Assistants." IEEE Access 9 (2021): 112821–36. http://dx.doi.org/10.1109/access.2021.3103893.

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32

Bickmore, Timothy W., Ha Trinh, Stefan Olafsson, et al. "Patient and Consumer Safety Risks When Using Conversational Assistants for Medical Information: An Observational Study of Siri, Alexa, and Google Assistant." Journal of Medical Internet Research 20, no. 9 (2018): e11510. http://dx.doi.org/10.2196/11510.

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33

Agarwal, Vineet, and Anjali Shukla. "Chatbot for Interview." International Journal of Recent Technology and Engineering (IJRTE) 11, no. 2 (2022): 46–49. http://dx.doi.org/10.35940/ijrte.b7092.0711222.

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Анотація:

The advent of virtual assistants has made communicating with computers a reality. Chatbots are virtual assistant tools designed to simplify the communication between humans and computers. A chatbot will answer your queries and execute a certain computation if required. Chatbots can be developed using Natural Language Processing (NLP) and Deep Learning. Natural Language Process technique like Naïve bayes can be used. Chatbot can be implemented for a fun purpose like chit-chat; these are called Conversational chatbots. Chatbots designed to answer any questions is known as horizontal chatbots and

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Gurney, Nikolos, George Loewenstein, and Nick Chater. "Conversational technology and reactions to withheld information." PLOS ONE 19, no. 4 (2024): e0301382. http://dx.doi.org/10.1371/journal.pone.0301382.

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People frequently face decisions that require making inferences about withheld information. The advent of large language models coupled with conversational technology, e.g., Alexa, Siri, Cortana, and the Google Assistant, is changing the mode in which people make these inferences. We demonstrate that conversational modes of information provision, relative to traditional digital media, result in more critical responses to withheld information, including: (1) a reduction in evaluations of a product or service for which information is withheld and (2) an increased likelihood of recalling that inf

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35

Frummet, Alexander, David Elsweiler, and Bernd Ludwig. "“What Can I Cook with these Ingredients?” - Understanding Cooking-Related Information Needs in Conversational Search." ACM Transactions on Information Systems 40, no. 4 (2022): 1–32. http://dx.doi.org/10.1145/3498330.

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As conversational search becomes more pervasive, it becomes increasingly important to understand the users’ underlying information needs when they converse with such systems in diverse domains. We conduct an in situ study to understand information needs arising in a home cooking context as well as how they are verbally communicated to an assistant. A human experimenter plays this role in our study. Based on the transcriptions of utterances, we derive a detailed hierarchical taxonomy of diverse information needs occurring in this context, which require different levels of assistance to be solve

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36

Dhanda, Armaan, Raman Goel, Sachin Vashisht, and Seba Susan. "Hindi Conversational Agents for Mental Health Assistance." International Journal of Applied Research on Information Technology and Computing 12, no. 1to3 (2021): 12–20. http://dx.doi.org/10.5958/0975-8089.2021.00004.x.

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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 re

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Asfoura, Evan, Gamal Kassem, Belal Alhuthaifi, and Fozi Belhaj. "Developing Chatbot Conversational Systems & the Future Generation Enterprise Systems." International Journal of Interactive Mobile Technologies (iJIM) 17, no. 10 (2023): 155–75. http://dx.doi.org/10.3991/ijim.v17i10.37851.

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Conversational technology has recently emerged effectively; it helps people in communicating with smart devices such as smartphones by using human language. When they emerged, they enabled and assisted users to perform various functions such as gathering information, conducting transactions, having general conversations, and easily navigating web services and entertainment. They not only have an impact on people in general by improving customer service as they can provide answers to any inquiries but also facilitated navigation by assisting people with disabilities by interacting with a system

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Almousa, Omar Saad, Hazem Migdady, and Mohammad Al-Talib. "Conversational Frames." International Journal of Embedded and Real-Time Communication Systems 11, no. 4 (2020): 104–33. http://dx.doi.org/10.4018/ijertcs.2020100106.

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This paper extends the previous work of Almousa and Migdady by proposing and implementing three models to improve the correctness and naturality of the given answers of smart personal assistants (SPAs). The motivation behind the three proposed models is the failure of some well-known existing SPAs (Siri and Salma) to extract contextual information from a conversation. The authors call this kind of information: the conversational frame. For evaluating the suggested models, the authors also implement an abstraction of the existing SPAs that they call non-framed model. To evaluate the four implem

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Agarwal, Sanchit, Jan Jezabek, Arijit Biswas, Emre Barut, Bill Gao, and Tagyoung Chung. "Building Goal-Oriented Dialogue Systems with Situated Visual Context." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (2022): 13149–51. http://dx.doi.org/10.1609/aaai.v36i11.21710.

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Goal-oriented dialogue agents can comfortably utilize the conversational context and understand its users' goals. However, in visually driven user experiences, these conversational agents are also required to make sense of the screen context in order to provide a proper interactive experience. In this paper, we propose a novel multimodal conversational framework where the dialogue agent's next action and their arguments are derived jointly conditioned both on the conversational and the visual context. We demonstrate the proposed approach via a prototypical furniture shopping experience for a m

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Nagargoje, Prof Vivek. "J.A.R.V.I.S Organizational Virtual Assistant." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 4664–67. http://dx.doi.org/10.22214/ijraset.2021.35066.

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Chatbots, or conversational interfaces as they are also known, present a new way for individuals to interact with computer systems. Traditionally, to get a question answered by a software program involved using a search engine, or filling out a form. A chatbot allows a user to simply ask questions in the same manner that they would address a human. The most well known chatbots currently are voice chatbots: Alexa and Siri. However, chatbots are currently being adopted at a high rate on computer chat platforms. The technology at the core of the rise of the chatbot is natural language processing

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Kim, Hyounghun, Doo Soon Kim, Seunghyun Yoon, Franck Dernoncourt, Trung Bui, and Mohit Bansal. "CAISE: Conversational Agent for Image Search and Editing." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (2022): 10903–11. http://dx.doi.org/10.1609/aaai.v36i10.21337.

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Demand for image editing has been increasing as users' desire for expression is also increasing. However, for most users, image editing tools are not easy to use since the tools require certain expertise in photo effects and have complex interfaces. Hence, users might need someone to help edit their images, but having a personal dedicated human assistant for every user is impossible to scale. For that reason, an automated assistant system for image editing is desirable. Additionally, users want more image sources for diverse image editing works, and integrating an image search functionality in

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Schreuter, Donna, Peter van der Putten, and Maarten H. Lamers. "Trust Me on This One: Conforming to Conversational Assistants." Minds and Machines 31, no. 4 (2021): 535–62. http://dx.doi.org/10.1007/s11023-021-09581-8.

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Deshmukh, Prof Anushree, Smit Shah, Heena Puthran, and Naisargi Shah. "Virtual Shopping Assistant for Online Fashion Store." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 110–17. http://dx.doi.org/10.22214/ijraset.2022.42099.

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Abstract: A new way for individuals to interact with computer systems will be done through chatbots or conversational interfaces. Historically, introducing a matter answered by a software package involves employing a program or filling out a type. The technology at the core of the increase of the chatbot is NLP i.e., Natural Language Processing. Sequence to Sequence (often abbreviated to seq2seq) fashions is a specific type of Recurrent Neural Network architectures that we commonly use (but no longer restricted) to clear up complicated Language issues like Machine Translation, Question Answeri

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Lee, Sungjin, and Rahul Jha. "Zero-Shot Adaptive Transfer for Conversational Language Understanding." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6642–49. http://dx.doi.org/10.1609/aaai.v33i01.33016642.

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Conversational agents such as Alexa and Google Assistant constantly need to increase their language understanding capabilities by adding new domains. A massive amount of labeled data is required for training each new domain. While domain adaptation approaches alleviate the annotation cost, prior approaches suffer from increased training time and suboptimal concept alignments. To tackle this, we introduce a novel Zero-Shot Adaptive Transfer method for slot tagging that utilizes the slot description for transferring reusable concepts across domains, and enjoys efficient training without any expl

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Adaimi, Rebecca, Howard Yong, and Edison Thomaz. "Ok Google, What Am I Doing?" Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 1 (2021): 1–24. http://dx.doi.org/10.1145/3448090.

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Conversational assistants in the form of stand-alone devices such as Amazon Echo and Google Home have become popular and embraced by millions of people. By serving as a natural interface to services ranging from home automation to media players, conversational assistants help people perform many tasks with ease, such as setting timers, playing music and managing to-do lists. While these systems offer useful capabilities, they are largely passive and unaware of the human behavioral context in which they are used. In this work, we explore how off-the-shelf conversational assistants can be enhanc

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Bandlamudi, Jayachandu, Kushal Mukherjee, Prerna Agarwal, et al. "Building Conversational Artifacts to Enable Digital Assistant for APIs and RPAs." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 22725–33. http://dx.doi.org/10.1609/aaai.v38i21.30306.

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In the realm of business automation, digital assistants/chatbots are emerging as the primary method for making automation software accessible to users in various business sectors. Access to automation primarily occurs through APIs and RPAs. To effectively convert APIs and RPAs into chatbots on a larger scale, it is crucial to establish an automated process for generating data and training models that can recognize user intentions, identify questions for conversational slot filling, and provide recommendations for subsequent actions. In this paper, we present a technique for enhancing and gener

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de Cock, Caroline, Madison Milne-Ives, Michelle Helena van Velthoven, Abrar Alturkistani, Ching Lam, and Edward Meinert. "Effectiveness of Conversational Agents (Virtual Assistants) in Health Care: Protocol for a Systematic Review." JMIR Research Protocols 9, no. 3 (2020): e16934. http://dx.doi.org/10.2196/16934.

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Background Conversational agents (also known as chatbots) have evolved in recent decades to become multimodal, multifunctional platforms with potential to automate a diverse range of health-related activities supporting the general public, patients, and physicians. Multiple studies have reported the development of these agents, and recent systematic reviews have described the scope of use of conversational agents in health care. However, there is scarce research on the effectiveness of these systems; thus, their viability and applicability are unclear. Objective The objective of this systemati

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Lin, Sheng-Chieh, Jheng-Hong Yang, Rodrigo Nogueira, Ming-Feng Tsai, Chuan-Ju Wang, and Jimmy Lin. "Multi-Stage Conversational Passage Retrieval: An Approach to Fusing Term Importance Estimation and Neural Query Rewriting." ACM Transactions on Information Systems 39, no. 4 (2021): 1–29. http://dx.doi.org/10.1145/3446426.

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Conversational search plays a vital role in conversational information seeking. As queries in information seeking dialogues are ambiguous for traditional ad hoc information retrieval (IR) systems due to the coreference and omission resolution problems inherent in natural language dialogue, resolving these ambiguities is crucial. In this article, we tackle conversational passage retrieval, an important component of conversational search, by addressing query ambiguities with query reformulation integrated into a multi-stage ad hoc IR system. Specifically, we propose two conversational query refo

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Li, Chang, and Hideyoshi Yanagisawa. "Intrinsic motivation in virtual assistant interaction for fostering spontaneous interactions." PLOS ONE 16, no. 4 (2021): e0250326. http://dx.doi.org/10.1371/journal.pone.0250326.

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With the growing utility of today’s conversational virtual assistants, the importance of user motivation in human–artificial intelligence interactions is becoming more obvious. However, previous studies in this and related fields, such as human–computer interaction, scarcely discussed intrinsic motivation (the motivation to interact with the assistants for fun). Previous studies either treated motivation as an inseparable concept or focused on non-intrinsic motivation (the motivation to interact with the assistant for utilitarian purposes). The current study aims to cover intrinsic motivation

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