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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 (April 18, 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 (June 18, 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 (February 22, 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 (March 22, 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 (May 16, 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 (January 28, 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 (November 7, 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 (April 2, 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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Nieminen, H., L. Kuosmanen, R. Bond, A. K. Vartiainen, M. Mulvenna, C. Potts, and C. Kostenius. "Coproducing multilingual conversational scripts for a mental wellbeing chatbot - where healthcare domain experts become chatbot designers." European Psychiatry 65, S1 (June 2022): S293. http://dx.doi.org/10.1192/j.eurpsy.2022.748.
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Introduction Digital mental health interventions, such as chatbots that promote mental health and wellbeing are a promising way to deliver low-threshold support 24/7 for those in need. According to current knowledge about the topic, health care professionals should participate in the design and development processes for digital interventions. Objectives The aim of this presentation is to describe the interdisciplinary content development process of the ChatPal chatbot. Methods The content development process started in co-operation with mental health professionals and potential users to identify requirements. Content was created, evaluated and tested in international, multi-disciplinary group workshops, and online tools were used to allow the collaboration. Initial conversational scripts were drafted in English, and translated into Finnish, Swedish and Scottish Gaelic. Results A multilingual chatbot was developed and the conversation scripts were structured and stored using a spreadsheet. The conversation scripts will be made freely available online in due course using this structured approach to formatting chatbot dialogue content. It will allow repurposing the content as well as facilitating studies that wish to assess the design of conversation scripts for mental health chatbots. Conversation design process also highlighted some challenges in turning empathetic and supportive conversations to short utterances suitable for a chatbot. Conclusions The ChatPal chatbot is now available in four languages. As literature about the topic is still scarce, it is important to describe and document the content development processes of mental health chatbots. Future work will develop a conversational UX toolkit that would allow health professionals to design chatbot scripts using design guidelines. Disclosure No significant relationships.
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Patel, Dhruv, NIhal Shetty, Paarth Kapasi, and Ishaan Kangriwala. "College Enquiry Chatbot using Conversational AI." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 903–15. http://dx.doi.org/10.22214/ijraset.2023.51324.
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Abstract: Chatbots are computer programs that use text or voice-based interfaces to replicate human conversation. They are often used to automate mundane processes, provide customer support, or aid in the retrieval of information. Chatbots are built with a number of strategies that enable them to interpret and respond to user inputs in a more human-like manner. They can be employed in a variety of industries, including e-commerce, healthcare, and banking. We have analysed and compared numerous chatbot strategies in this report to establish the optimal way for our own chatbot project. We reviewed twenty-six papers on chatbot development and assessed the advantages and disadvantages of various strategies. Natural language processing techniques, such as tokenization and named entity recognition, have been shown in our research to be critical for interpreting user inputs. We also discovered that dialogue management methods, such as rule-based and machine learning-based approaches, have an important influence in influencing discussion flow. Furthermore, we discovered that natural language generation techniques, such as template-based and neural network-based methods, are critical in generating effective chatbot responses. We also investigated various services on the market in order to create a functional chatbot for our college. We also emphasized the various applications of chatbots as well as the current hurdles in the industry. Based on these findings, we chose a technique for our own chatbot project that employs advanced natural language processing and machine learning techniques to create more human-like conversations and improve overall user experience.
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Al Fajri, Miftahul Rizki, and Budi Hartono. "Pengembangan Aplikasi Chatbot Telegram Menggunakan Framework Rasa untuk Pelayanan Administrasi di Perguruan Tinggi Universitas Stikubank." Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) 8, no. 1 (January 1, 2024): 133–36. http://dx.doi.org/10.35870/jtik.v8i1.1370.
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The administrative services in universities are currently still conventional, resulting in many difficulties due to the mismatched time. For this research, a Telegram chatbot application will be developed to provide various answers to administrative inquiries in universities automatically. The chatbot application will be developed using the Rasa Open Source framework. This research collects data from several frequently asked questions by students and prospective students to the Biro Administrasi Akademik dan Kemahasiswaan (BAAK) as the initial data for the chatbot application. From this data, it will be transformed into Natural Language Understanding (NLU) and dialogue data. The number of NLU sentence examples is 213, and there are 27 dialogue examples. As a result, the chatbot application can understand user inquiries well, but it experiences some errors in generating dialogue responses.
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Monteiro, Mateus de Souza, Gabriely Oliveira da Silva Batista, and Luciana Cardoso de Castro Salgado. "Investigating usability pitfalls in Brazilian and Foreign governmental chatbots." Journal on Interactive Systems 14, no. 1 (July 29, 2023): 331–40. http://dx.doi.org/10.5753/jis.2023.3104.
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The usability of chatbots is an essential aspect of e-government initiatives that aim to improve citizens’ access to public services. Despite significant efforts to enhance e-gov usability in Brazil, there is still a lack of research examining usability pitfalls in human-chatbot interaction. Therefore, this study aims to fill this research gap by presenting an analysis and comparison of ten electronic government chatbots, five national (Brazilian) and five foreign (1 Argentine, 1 Portuguese, 1 American, 1 British, and 1 Singaporean), using an adapted version of Heuristics for chatbots. This study examines the design issues and opportunities that can affect the usability and adoption of e-government chatbots in Brazil. These include enhancing the navigation mechanism for dialogues, improving response times to requests, clearly indicating the end of the dialogue, providing guidance on inputting utterances correctly and implementing standardized utterances for the same actions.
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Kim, Sihyung, Oh-Woog Kwon, and Harksoo Kim. "Knowledge-Grounded Chatbot Based on Dual Wasserstein Generative Adversarial Networks with Effective Attention Mechanisms." Applied Sciences 10, no. 9 (May 11, 2020): 3335. http://dx.doi.org/10.3390/app10093335.
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A conversation is based on internal knowledge that the participants already know or external knowledge that they have gained during the conversation. A chatbot that communicates with humans by using its internal and external knowledge is called a knowledge-grounded chatbot. Although previous studies on knowledge-grounded chatbots have achieved reasonable performance, they may still generate unsuitable responses that are not associated with the given knowledge. To address this problem, we propose a knowledge-grounded chatbot model that effectively reflects the dialogue context and given knowledge by using well-designed attention mechanisms. The proposed model uses three kinds of attention: Query-context attention, query-knowledge attention, and context-knowledge attention. In our experiments with the Wizard-of-Wikipedia dataset, the proposed model showed better performances than the state-of-the-art model in a variety of measures.
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SENAPATI, ATHARVA. "Psychological Emotion Recognition Of Students Based Chatbot." International Journal of Applied and Advanced Multidisciplinary Research 1, no. 3 (December 14, 2023): 267–77. http://dx.doi.org/10.59890/ijaamr.v1i3.565.
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In the context of modern education, where students' emotional well-being plays a crucial role in academic success, the integration of technology, particularly machine learning-based chatbots, presents a promising avenue to support and recognize the psychological states of students. This paper delves into the development and evaluation of a chatbot system designed to recognize and respond to the emotions expressed by students. Leveraging natural language processing and sentiment analysis, the chatbot engages in conversations with students, allowing for the real-time recognition of emotional states. Our study not only focuses on the technical aspects of emotion recognition but also explores the implications and ethical considerations of deploying such technology in educational settings. By shedding light on the potential of machine learning-based chatbots to enhance emotional support and understanding within educational environments, this research contributes to the ongoing dialogue on student well-being and the role of technology in education.
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Li, Lixin. "Studies advanced in chatbots based on deep learning." Applied and Computational Engineering 6, no. 1 (June 14, 2023): 799–804. http://dx.doi.org/10.54254/2755-2721/6/20230921.
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Chatbots have always been a hot research topic in the field of human-computer interaction research, which aims to build a conversational intelligent response model to simulate human dialogue. Thanks to the rapid development of natural language processing technology and the continuous accumulation of dialogue data, the research of chat robots have made remarkable progress, which has gradually been widely used in various fields such as e-commerce and smart home. According to different technical frameworks, existing chatbots are mainly divided into two types: retrieval chatbots and generative chatbots. As the primary means of implementing chatbots in the industry, retrieval chatbots have smooth responses and low computational resource consumption. In contrast, generative chatbots do not require a predefined knowledge base and can dynamically generate responses based on the dialogue content. In this paper, focusing on the above two types of frameworks, we introduce the latest research progress in the field of deep learning-based chatbots in detail, including the representative algorithms and corresponding pipelines. Second, we compare the performance of representative algorithms on different datasets. We also summarize the problems chatbot technology research faces and give an outlook on its future development trends.
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Safi, Zeineb, Alaa Abd-Alrazaq, Mohamed Khalifa, and Mowafa Househ. "Technical Aspects of Developing Chatbots for Medical Applications: Scoping Review." Journal of Medical Internet Research 22, no. 12 (December 18, 2020): e19127. http://dx.doi.org/10.2196/19127.
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Background Chatbots are applications that can conduct natural language conversations with users. In the medical field, chatbots have been developed and used to serve different purposes. They provide patients with timely information that can be critical in some scenarios, such as access to mental health resources. Since the development of the first chatbot, ELIZA, in the late 1960s, much effort has followed to produce chatbots for various health purposes developed in different ways. Objective This study aimed to explore the technical aspects and development methodologies associated with chatbots used in the medical field to explain the best methods of development and support chatbot development researchers on their future work. Methods We searched for relevant articles in 8 literature databases (IEEE, ACM, Springer, ScienceDirect, Embase, MEDLINE, PsycINFO, and Google Scholar). We also performed forward and backward reference checking of the selected articles. Study selection was performed by one reviewer, and 50% of the selected studies were randomly checked by a second reviewer. A narrative approach was used for result synthesis. Chatbots were classified based on the different technical aspects of their development. The main chatbot components were identified in addition to the different techniques for implementing each module. Results The original search returned 2481 publications, of which we identified 45 studies that matched our inclusion and exclusion criteria. The most common language of communication between users and chatbots was English (n=23). We identified 4 main modules: text understanding module, dialog management module, database layer, and text generation module. The most common technique for developing text understanding and dialogue management is the pattern matching method (n=18 and n=25, respectively). The most common text generation is fixed output (n=36). Very few studies relied on generating original output. Most studies kept a medical knowledge base to be used by the chatbot for different purposes throughout the conversations. A few studies kept conversation scripts and collected user data and previous conversations. Conclusions Many chatbots have been developed for medical use, at an increasing rate. There is a recent, apparent shift in adopting machine learning–based approaches for developing chatbot systems. Further research can be conducted to link clinical outcomes to different chatbot development techniques and technical characteristics.
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Rogovets, A. Yu, A. Mazurkevitch, I. Y. Suvorova, M. Vinnik, and A. A. Prikhodko. "Chat-Bot as a Way to Support Victims of School Bullying." Современная зарубежная психология 12, no. 3 (October 27, 2023): 103–14. http://dx.doi.org/10.17759/jmfp.2023120310.
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<p style="text-align: justify;">This article is devoted to an overview of the sources on therapeutic chatbots, as well as a description of the problem of creating an anti-bullying chatbot. Most chatbots work on the basis of cognitive-behavioral psychotherapy and are aimed at preventing depressive vessels in adults. There are no anti-bullying chatbots yet. There are at least two reasons: the exceptional situation of bullying and the legal aspects of working with teenagers. The specificity of the bullying situation is working with the patient in real life and health conditions. Trauma-focused Cognitive Behavioral Therapy is the most appropriate method of dealing with victims of school transportation. The legal aspect includes the signing of a data revision resolution and the status of a minor in the conclusion of legal negotiations. The article proposes the idea of an author's chatbot, also based on Cognitive Behavioral Therapy and psychological first aid. The author's dialogue agent includes three blocks: work on emotional manifestations, psychoeducation and acute stress relief. In total, the algorithm includes five emotional channels - four dysfunctional and single-functional. The severity of emotional disturbances is measured on a five-point scale and a mood log, by detecting which you can track the effectiveness of the chatbot. A chatbot cannot implement all the functions of therapy, but it can help reduce the level of emotional stress.</p>
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Anki, Prasnurzaki, and Alhadi Bustamam. "Measuring the accuracy of LSTM and BiLSTM models in the application of artificial intelligence by applying chatbot programme." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 1 (July 1, 2021): 197. http://dx.doi.org/10.11591/ijeecs.v23.i1.pp197-205.
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Python programme contains a question and answer system that derived from data sets that have used and implemented the chatbot in this modern era. where the data collected is in the form of corpuses containing extensive metadata-rich fictional conversations derived from extracted film scripts, commonly called cornell movie dialogue corpus. The various models have been used chatbots in python programmes, and LSTM and BiLSTM models were specifically used in this study. Where the form of accuracy will be reported as a result of the implementation of LSTM and BiLSTM models in the chatbot programme. The programme performance will be influenced by the data from the model selection, because the level of accuracy is determined by the target programme being taken. So this is the main factor that determines which model to choose. Based on considerations required for choosing the programme model, in the end the LSTM and the BiLSTM models are chosen and will be applied to the programme. Based on the LSTM and BiLSTM chatbot programmes that have been tested, it can be concluded that the best parameters come from a pair of BiLSTM chatbots using the BiLTSM model with an average accuracy value of 0.995217.
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Mavrodieva, Ivanka. "Linguistic and Rhetorical Features of Dialogue on Rhetorical Topics between a Human and Chatbot GPT." Rhetoric and Communications, no. 56 (July 30, 2023): 22–45. http://dx.doi.org/10.55206/cikp7841.
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Abstract: This paper presents the results of an analysis of a dialogue between a human and a chatbot on rhetorical topics. The problematic has a topicality that is new in the study of an understudied field, namely the communication in a mini-virtual community between a human and GhatGTP. The first focus is on analyzing linguistic and rhetorical features of answering questions posed by the researcher, who is also a participant in the dialogue. The second focus is on the ways in which the results of the search for rhetoric-related information are presented and structured by the chatbot. The third focus is on the ways in which the chatbot identifies itself using algorithms and pre-prepared information by experts from different fields and how it verbalizes and self-assesses it. The first hypothesis is that from a linguistic point of view the chatbot uses terms; does not allow figurative language, realizes an informative function; structures short texts of good logical consistency, which are dominated by the statement of previously presented information. The second hypothesis is related to rhetorical themes and canons and it is that the chatbot successfully realizes two rhetorical canons (invention and composition) searching for information from accessible online sources, selects and structures facts into popular level. The paper tests a research approach, conventionally called auto-cyberethnographic monitoring, which combines the cyberethnographic method with autoethnography. The text does not aim at providing exhaustive information, it is oriented towards establishing the possibilities of delineating a new scientific field of research that presupposes an interdisciplinary approach and modern research methods. Keywords: rhetoric, rhetorical canons, language, dialogue, Chatbot GPT, cyberethnographic method, autoethnography, auto-cyberethnographic monitoring. Rhetoric and Communications Journal, issue 56, July 2023
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Deka, Debajani. "Artificial intelligence and mental health: a review article." International Journal of Research in Medical Sciences 12, no. 4 (March 29, 2024): 1317–20. http://dx.doi.org/10.18203/2320-6012.ijrms20240864.
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The term AI was originally coined by a computer scientist, John McCarthy, who defined it as the science and engineering of making intelligent machines. The father of AI authored a 1950 article, “Computing Machinery and intelligence” that discussed the reasons for considering a machine to be intelligent. Artificial intelligence is useful to facilitate faster disease detection. It helps to understand disease progression, improve medication/treatment dosages, and discover innovative treatments. The artificial intelligence tools mostly used for psychosis risk screening are chatbots and large-scale social media data analysis. Chatbot is a computer program that allows human-computer interactions in the form of textual dialogue based on the technology of natural language processing. The world's first chatbot, ELIZA, was designed in the 1960s and responds to special rules by recognizing keywords in user-entered text. Chatbots in the mental healthcare field include Tess, Florence, Buoy Health, and Your. Md. In addition to natural language processing, the machine learning methods adopted by chatbots also include natural language understanding, artificial neural networks, and recurrent neural networks.
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Alhassan, Nadrh Abdullah, Abdulaziz Saad Albarrak, Surbhi Bhatia, and Parul Agarwal. "A Novel Framework for Arabic Dialect Chatbot Using Machine Learning." Computational Intelligence and Neuroscience 2022 (March 10, 2022): 1–11. http://dx.doi.org/10.1155/2022/1844051.
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With the advent of artificial intelligence and proliferation in the demand for an online dialogue system, the popularity of chatbots is growing on various industrial platforms. Their applications are getting widely noticed with intelligent tools as they are able to mimic human behavior in natural languages. Chatbots have been proven successful for many languages, such as English, Spanish, and French, over the years in varied fields like entertainment, medicine, education, and commerce. However, Arabic chatbots are challenging and are scarce, especially in the maintenance domain. Therefore, this research proposes a novel framework for an Arabic troubleshooting chatbot aiming at diagnosing and solving technical issues. The framework addresses the difficulty of using the Arabic language and the shortage of Arabic chatbot content. This research presents a realistic implementation of creating an Arabic corpus for the chatbot using the developed framework. The corpus is developed by extracting IT problems/solutions from multiple domains and reliable sources. The implementation is carried forward towards solving specific technical solutions from customer support websites taken from different well-known organizations such as Samsung, HP, and Microsoft. The claims are proved by evaluating and conducting experiments on the dataset by comparing with the previous researches done in this field using different metrics. Further, the validations are well presented by the proposed system that outperforms the previously developed different types of chatbots in terms of several parameters such as accuracy, response time, dataset data, and solutions given as per the user input.
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Nordheim, Cecilie Bertinussen, Asbjørn Følstad, and Cato Alexander Bjørkli. "An Initial Model of Trust in Chatbots for Customer Service—Findings from a Questionnaire Study." Interacting with Computers 31, no. 3 (May 1, 2019): 317–35. http://dx.doi.org/10.1093/iwc/iwz022.
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Abstract Chatbots are predicted to play a key role in customer service. Users’ trust in such chatbots is critical for their uptake. However, there is a lack of knowledge concerning users’ trust in chatbots. To bridge this knowledge gap, we present a questionnaire study (N = 154) that investigated factors of relevance for trust in customer service chatbots. The study included two parts: an explanatory investigation of the relative importance of factors known to predict trust from the general literature on interactive systems and an exploratory identification of other factors of particular relevance for trust in chatbots. The participants were recruited as part of their dialogue with one of four chatbots for customer service. Based on the findings, we propose an initial model of trust in chatbots for customer service, including chatbot-related factors (perceived expertise and responsiveness), environment-related factors (risk and brand perceptions) and user-related factors (propensity to trust technology). RESEARCH HIGHLIGHTS We extend the current knowledge base on natural language interfaces by investigating factors affecting users’ trust in chatbots for customer service. Chatbot-related factors, specifically perceived expertise and responsiveness, are found particularly important to users’ trust in such chatbots, but also environment-related factors such as brand perception and user-related factors such as propensity to trust technology. On the basis of the findings, we propose an initial model of users’ trust chatbots for customer service.
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Kotsiubivska, Kateryna, Olena Tymoshenko, Mykola Huziy, and Vladyslav Lysyniuk. "Concept of Chatbot as an Intelligent Dialogue Assistant." Digital Platform: Information Technologies in Sociocultural Sphere 6, no. 2 (November 13, 2023): 329–40. http://dx.doi.org/10.31866/2617-796x.6.2.2023.293598.
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The purpose of the study is to compare chatbots, assess their ability to perform tasks, analyze their impact on consumers, and determine the areas of application and risks associated with the use of intelligent dialogue assistants. The research methods used in the study include analysis and systematisation of information, synthesis and generalization of theoretical data based on a review of current research, which made it possible to assess the risks and benefits of using chatbots for businesses and ordinary users. The scientific novelty lies in the analysis of technologies for creating intelligent dialogue assistants and determining the areas of application of chatbots. A study of the areas of application of modern intelligent dialogue systems has shown the high efficiency of using chatbots to solve typical user problems. The article provides statistical data on how satisfied Internet users are with contacts with chatbots and describes recommendations for improving such dialogue systems. An analysis of the role of artificial intelligence in the operation of dialogue systems has shown possible risks from the impact of technology on the minds of users. Conclusions. One of the most obvious trends in chatbots in 2023 is that their use will become more widespread and chatbots themselves will become more sophisticated. Their advantage is free customer service and data collection, which can then be used for marketing research. Over time, chatbots will be used in such areas as marketing, recruitment, education, and medicine. Their ability to perform a wide range of tasks makes chatbots attractive for e-commerce stores, B2B companies, real estate, healthcare, and education. In the future, intelligent dialogue agents will work with huge, dynamic, heterogeneous data streams, providing powerful capabilities for adaptive and flexible interaction. Dialogue systems have become successful and reliable due to the large amount of real-world user data available to their developers. However, these systems still exhibit rather limited communication behaviour modelled on information retrieval tasks. Chatbots are developed for research purposes, but they are often limited to a narrow, manually created domain. The newest trend in conversational agent development involves neural networks and models trained on huge collections of dialogue data without detailed specifications of dialogue states. These models lack tractability and interpretability due to their black-box nature. They also require extensive supervised training data to be competitive.
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Jayavardhana, Arya, and Samuel Ady Sanjaya. "A Systematic Literature Review: A Comparison Of Available Approaches In Chatbot And Dialogue Manager Development." International Journal of Science, Technology & Management 4, no. 6 (November 29, 2023): 1441–50. http://dx.doi.org/10.46729/ijstm.v4i6.983.
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The present study reviewed a number of articles chosen from a screening and selecting process on the various different methods that can be used in the context of chatbot development and dialogue managers. Since chatbots have seen a significant rise in popularity and have played an important role in helping humans complete daily tasks, this systematic literature review (SLR) aims to act as a guidance for future research. During the process of analyzing and extracting data from the 13 articles chosen, it has been identified that Artificial Neural Network (ANN), Ensemble Learning, Recurrent Neural Network (RNN), and Long-Short Term Memory (LSTM) is among some of the most popular algorithms used for developing a chatbot. Where all of these algorithms are suitable for each unique use case where it offers different advantages when implemented. Other than that, dialogue managers lean more towards the field of Deep Reinforcement Learning (DRL), where Deep Q-Networks (DQN) and its variants such as Double Deep-Q Networks (DDQN) and DDQN with Personalized Experience Replay (DDQN-PER) is commonly used. All these variants have different averages on episodic reward and dialogue length, along with different training time needed which indicates the computational power needed. This SLR aims to identify the methods that can be used and identify the best proven method to be applied in future research.
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Poonawala, Raj, Sanjana Shinde, Sandeep Kadam, Sarth Raut, and Shika Sharma. "Chatbot in Artificial Intelligence." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 2096–100. http://dx.doi.org/10.22214/ijraset.2023.50467.
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Abstract: This paper discloses a virtual conversational method and system to relieve the psychological stress of adolescents. It also aims at providing some positive information through continuous dialogue answers in order to guide adolescents to think and face difficulties with a positive and optimistic attitude and the agenda of reliving the psychological pressure of the adolescents can be achieved. Conventional face-to-face stress detection and relief methods do not work when confronted with those adolescents who are reluctant to express their negative emotions to the people in real life. In this paper, we would like to present an adolescent- oriented intelligent conversational chatting system called “HappySoul”, which acts as a virtual friend who can assist to encourage, understand, comfort, and guide stressful adolescents to pour out their bad and negative feelings, thereby releasing the stress. Chatbots, or conversational interfaces, present a new way for adolescents to interact with computer systems. This chatbot will allow a user to simply ask questions in the same way that they would address a human. The technology at the core of the proposed chatbot is natural language processing (“NLP”), RNN and client server architecture with the help of Android GUI.
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Sysoyev, Pavel V., Evgeny M. Filatov, and Danila O. Sorokin. "Chatbots in Foreign Language Teaching: Current Issues and Prospects for Forthcoming Research." Linguistics and Intercultural Communication 26, no. 3_2023 (November 29, 2023): 46–59. http://dx.doi.org/10.55959/msu-2074-1588-19-26-3-3.
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The current stage is characterized by the dynamic development of artificial intelligence technologies and their introduction into foreign language teaching. Chatbots are one of such dialogical interactive teaching programs capable of developing foreign-language oral and written speech skills of a learner by maintaining a dialogue with them and imitating human speech on the basis of natural language and machine learning technologies and the algorithms of human speech embedded in the programme. The present paper is based on the analysis and synthesis of methodological studies devoted to the development of methods of teaching pupils and students foreign-language oral and written speech interaction based on chatbots. The most frequently discussed aspects in the problems of modern research are identified, and the prospects for further research related to the use of chatbots in teaching a foreign language are defined. The most commonly discussed aspects in methodological papers include: a) identifying learners’ attitudes towards the use of chatbots; b) identifying the possibility of developing learners’ written and oral speech skills based on chatbots; c) determining the level of learners’ proficiency in a foreign language for educational interaction with a chatbot. As prospective research, we identify studies aimed at a) developing step-by-step methods of teaching foreign language speech interaction based on chatbots; d) defining the nomenclature of foreign language speech interaction skills with a chatbot.
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Kang, Hoon-chul, Myeong-Cheol Jwa, and Jeong-Woo Jwa. "The task-oriented Smart Tourism Chatbot Service." International Journal of Membrane Science and Technology 10, no. 4 (September 7, 2023): 235–43. http://dx.doi.org/10.15379/ijmst.v10i4.1888.
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The smart tourism service provides tourists with a travel planner service before the trip and a tour guide service during the trip. Smart tourism chatbot service can easily and conveniently provide smart tourism services to tourists along with smart tourism apps. In this paper, we develop the smart tourism platform and propose the task-oriented smart tourism chatbot service that efficiently provides tourism information provided by smart tourism apps to users. The smart tourism platform consists of the smart tourism chatbot and smart tourism information systems. The smart tourism chatbot system identifies the intention of the user's question and searches for the tourism information ID in the tourism information knowledgebase. The smart tourism information system provides tourism information of the smart tourism app to the user using the searched tourism information ID. The proposed smart tourism chatbot system applies the tourism information NER (Named Entity Recognition) model to accurately understand the intention of the user's question to the existing DST (Dialogue State Tracking) model-based chatbot system. We create tourist information named entities of the NER model based on the values of the domain and slot in the DST model defined in the 4W1H method. The NER model determines the domain, slot, and value to identify the intention of the user's question, and the DST model manages the dialogue state and retrieves the tourism information ID from the tourism information knowledgebase.
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Anindyati, Laksmi. "Analisis dan Perancangan Aplikasi Chatbot Menggunakan Framework Rasa dan Sistem Informasi Pemeliharaan Aplikasi (Studi Kasus: Chatbot Penerimaan Mahasiswa Baru Politeknik Astra)." Jurnal Teknologi Informasi dan Ilmu Komputer 10, no. 2 (April 14, 2023): 291. http://dx.doi.org/10.25126/jtiik.20231026409.
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<p>Chatbot menjadi suatu kebutuhan bisnis yang membutuhkan pelayanan interaksi secara <em>real-time</em> dan 24 jam. Kebutuhan tersebut juga diperlukan saat penerimaan mahasiswa baru di Politeknik Astra. Chatbot dapat menjadi salah satu penyedia informasi yang interaktif untuk calon mahasiswa Politeknik Astra, ketika mencari informasi terkait proses pendaftaran mahasiswa baru maupun terkait Politeknik Astra secara umum. Proses analisis dan perancangan sistem dilakukan, dimulai dengan studi literatur. Hasil dari studi literatur dipilihlah Framework RASA yang akan digunakan dalam pengembangan chatbot. Framework RASA memiliki performa yang baik karena memiliki Rasa NLU dan Rasa CORE. Rasa NLU sebagai basis library yang membangunteraksi antara komputer dan manusia dengan menerapkan dua metode dan algoritma kecerdasan buatan yaitu pemrosesan bahasa alami dan mesin pembelajaran. Rasa NLU bertanggung jawab membuat interaksi lebih nyata, pengguna layanan akan merasakan interaksi langsung seperti dengan manusia bukan dengan komputer. Rasa CORE juga berperan dalam membuat interaksi terasa nyata, dengan mengatur interaksi dialog antara antara bot (komputer dibalik chatbot) dengan pengguna. Framework Rasa juga bersifat <em>open source</em> sehingga memiliki adaptabilitas yang tinggi ketika diperlukan modifikasi untuk menyesuaikan kebutuhan bisnis yang ada. Proses pengembangan sistem dilanjutkan dengan melakukan analisis dan perancangan sistem informasi penunjang. Sistem informasi penunjang chatbot ini dibangun untuk mengakomodir kebutuhan proses CRUD (<em>Create, Read, Update</em> dan <em>Delete</em>) dari pertanyaan – respon yang nantinya dipelajari oleh chatbot sebelum dapat berinteraksi seperti manusia. Sistem informasi penunjang ini membantu penyesuaian konfigurasi chatbot dalam merespon pertanyaan sehingga operasional kebutuhan tersebut dapat mudah dilakukan oleh admin tanpa latar belakang IT. Hasil dari penelitian ini adalah kebutuhan sistem yang direpresentasikan pada use case diagram dan flowchart lalu pemilihan pipeline NLU untuk chatbot, arsitektur sistem, perancangan database dalam bentuk physical data model, dan perancangan desain antarmuka (mockup) sistem penunjang chatbot framework RASA.</p><p> </p><p><em><strong>Abstract</strong></em></p><p><em>Chatbots have become essential in business that requires interaction with customers in real-time and 24 hours. The requirements have become a necessity in Polytechnic Astra especially during the acceptance period of new students. Chatbot can be an interactive provider of information to prospective students of Polytechnic Astra who are looking information about the registration process or information related to Polytechnic Astra in general. The analysis and design process are conducted, starting with study literature. The results of the literature study, the RASA Framework were chosen as a tool to develop chatbots. RASA Framework performs well with the Rasa NLU and Rasa Core. Rasa NLU as a base library to build interactions between computers and humans using artificial intelligence. Rasa NLU is responsible for making interaction much real, like direct interaction with humans. Rasa core is a base library to regulate the interaction dialogue between chatbots and users. The Rasa Framework is also open source, so it has high adaptability to be modified to suit existing business needs. This supporting information system help to adjust the configuration chatbot in responding to questions, so that the operational can be easily carried out by the admin without IT background. The results of this research are the system requirements represented in use case diagrams and flowcharts and the selection of NLU pipeline for chatbots, the system architecture, the database design in the form of physical data models, and the interface design (mockup) for the chatbot framework support system RASA.</em></p>
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Sysoyev, Pavel V., and Evgeniy M. Filatov. "Method of the development of students' foreign language communication skills based on practice with a chatbot." Perspectives of Science and Education 63, no. 3 (June 1, 2023): 201–18. http://dx.doi.org/10.32744/pse.2023.3.13.
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Introduction. Chatbot is a program based on machine learning and natural language that allows the organisation of learners’ foreign language communication practice. However, the effectiveness of the teaching method depends on how and to what extent the foreign language practice with the chatbot is integrated into the foreign language learning process. The aim of the study is to develop and test the author's step-by-step method of the development of learners' foreign language communication skills based on chatbot practice. Materials and methods. Participants of the research were 1st year students of Derzhavin Tambov State University (Russian Federation), majoring in English as a Foreign Language. Students of the control group (N=25) were trained in foreign-language speech communication in traditional foreign language classes, and participants of the experimental group (N=25) in addition to regular classes for a semester once a week participated in a foreign language communication practice with a chatbot and performed tasks for the analysis of practice results. Ten foreign language speech skills were under controlled. The data analysis was carried out using the Student’s t-test. Research results. The step-by-step method of the development of students' foreign language communication skills based of practice with a chatbot proved to be effective with regard to the development of the following skills: asking for information on the topic of the dialogue (t = 2.49; p ≤ 0.05); responding to a dialogue partner's requests (t = 2.24; p ≤ 0.05); stating thoughts on the topic of the dialogue in short or extended form (t = 1.78; p ≤ 0.05); drawing conclusions (t = 1, 8; p ≤ 0.05) Expression of students’ own opinion on the topic of discussion (t = 1.91; p ≤ 0.05); providing arguments for their opinion on the topic of discussion (t = 1.41; p ≤ 0.05); interrogation, clarification, reformulation, expression ideas by other language means (t = 4, 11; p ≤ 0.05). The study did not show the effectiveness of the proposed method on the development of students' abilities to initiate a dialogue (t = 0.58; p> 0.05); welcome dialogue participants and exchange greeting phrases (t = 0.58; p> 0.05); end a conversation (t = 0.58; p> 0.05) because of students' high proficiency in these skills at the time of participation in the experiment. Conclusion. The novelty of the study is in the development of a step-by-step method of the development of learners' foreign language communication skills based on practice with a chatbot. The results of the study can be used in foreign language teaching to secondary school university level students.
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Qin, Zhen (Luther). "Conversational Breakdown Detector for a Motivational Interviewing Conversational Agent." IJournal: Student Journal of the Faculty of Information 9, no. 1 (December 19, 2023): 60–77. http://dx.doi.org/10.33137/ijournal.v9i1.42237.
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A conversational breakdown in human-chatbot interaction refers to a disruption or failure in the communicative flow between the human user and the chatbot. To recover a disrupted conversation, the first step is to detect the breakdown. Researchers have proposed methods using supervised learning and semi-supervised learning in dialogue systems to achieve the goal of detecting conversational breakdown. However, few studies have focused on detecting breakdowns in automated therapeutic conversations, especially conversations led by motivational interviewing chatbots. The presence of conversational breakdowns has negative impacts on the human-chatbot interaction, such as frustration, dissatisfaction, or loss of trust. This gap suggests a need to build a robust and efficient conversational breakdown detector that recognizes interruptions during the conversation. Conversational breakdown detection paves the way for further action to recover conversations. In this paper, I develop a novel, unifying framework called “CIMIC” for characterizing the conversational breakdowns of “MIBot,” a motivational interviewing conversational agent for smoking cessation. I collect 200 pieces of conversational data through Prolific and annotate them using the CIMIC framework with a group of four trained annotators. The annotated dataset is then applied as the training set to fine-tune GPT-3 models to build a conversational breakdown detector for the MIBot.
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Fiialka, Svitlana, Zoia Kornieva, and Tamara Honcharuk. "The use of ChatGPT in creative writing assistance." XLinguae 17, no. 1 (January 2024): 3–19. http://dx.doi.org/10.18355/xl.2024.17.01.01.
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This paper explores the integration of ChatGPT, an advanced AI language model, into creative writing. The paper investigates the capabilities of ChatGPT in generating novel story ideas, characters, plots, and stylistic elements such as metaphors and dialogue within various genres, including narrative, poetry, and drama. With its generative potential, ChatGPT is a valuable tool to simplify the creative writing process, providing authors with innovative concepts and supporting material that can be further developed and refined. Key findings of the study include ChatGPT's ability to craft detailed character portraits, engage in realistic dialogue, and produce atmospheric descriptions. While the chatbot can occasionally produce repetitive or biased results, careful human curation and interaction with the model can mitigate these issues and improve the writing process. The paper concludes that, despite limitations, AI agents like ChatGPT can significantly reinforce human creativity in writing, provided they are used as inspiration rather than a replacement for human ingenuity. The research underlines the importance of research on AI and creativity, especially regarding the ethical implications and the balance between human and machine contributions to art.
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Rodríguez-Cantelar, Mario, Marcos Estecha-Garitagoitia, Luis Fernando D’Haro, Fernando Matía, and Ricardo Córdoba. "Automatic Detection of Inconsistencies and Hierarchical Topic Classification for Open-Domain Chatbots." Applied Sciences 13, no. 16 (August 8, 2023): 9055. http://dx.doi.org/10.3390/app13169055.
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Current State-of-the-Art (SotA) chatbots are able to produce high-quality sentences, handling different conversation topics and larger interaction times. Unfortunately, the generated responses depend greatly on the data on which they have been trained, the specific dialogue history and current turn used for guiding the response, the internal decoding mechanisms, and ranking strategies, among others. Therefore, it may happen that for semantically similar questions asked by users, the chatbot may provide a different answer, which can be considered as a form of hallucination or producing confusion in long-term interactions. In this research paper, we propose a novel methodology consisting of two main phases: (a) hierarchical automatic detection of topics and subtopics in dialogue interactions using a zero-shot learning approach, and (b) detecting inconsistent answers using k-means and the Silhouette coefficient. To evaluate the efficacy of topic and subtopic detection, we use a subset of the DailyDialog dataset and real dialogue interactions gathered during the Alexa Socialbot Grand Challenge 5 (SGC5). The proposed approach enables the detection of up to 18 different topics and 102 subtopics. For the purpose of detecting inconsistencies, we manually generate multiple paraphrased questions and employ several pre-trained SotA chatbot models to generate responses. Our experimental results demonstrate a weighted F-1 value of 0.34 for topic detection, a weighted F-1 value of 0.78 for subtopic detection in DailyDialog, then 81% and 62% accuracy for topic and subtopic classification in SGC5, respectively. Finally, to predict the number of different responses, we obtained a mean squared error (MSE) of 3.4 when testing smaller generative models and 4.9 in recent large language models.
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Maulidiyah, Ismi Wafda, Zaqiatul Mardiah, and Dikri Dirwatul Ghozali. "Pelanggaran Maksim dan Strategi Pelanggaran yang Terjadi pada Dialog Aplikasi Chatbot Simsimi Bahasa Arab dan Inggris." Jurnal Alfazuna : Jurnal Pembelajaran Bahasa Arab dan Kebahasaaraban 4, no. 2 (June 19, 2020): 189–211. http://dx.doi.org/10.15642/alfazuna.v4i02.593.
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In the era of technology that continues to develop, the way to communicate also develops. One of them is the chatbot as a friend of human communication. This research examines a chatbot that can make users laugh or upset because of the uniqueness of its responses. That is SimSimi, an artificial intelligence. This research examines the extent of SimSimi can communicate effectively and efficiently. In answering this matter, this research analyzes in what topics the flouting maxims are mostly committed by SimSimi. The analysis process relies on the principle of cooperation Grice (1975). Further, each flouting maxims is explored about the strategy of violations that occur, by referring to the idea of Cutting (2002) about the maxim violation strategy. In addition, with SimSimi's multilingual expertise, this research also compares the differences in the frequency of flouting maxims by SimSimi in Arabic and English. Therefore, this research utilizes comparative studies using qualitative methods. The conversations analyzed were 40 conversations containing 4 topics. Based on 20 Arabic conversations, there were 32 flouting maxims. As for the 20 conversations in English, there were 30 flouting maxims. This shows the comparison of the frequency of flouting maxims in Arabic and English dialogue at 16:15. As for the strategies of flouting maxims that occurred, there are 10 kinds of strategies in Arabic dialogue and 6 kinds of strategies in English dialogue. In Arabic dialogues, the topic that contains the most violations of the maxims is the topic of religion. So it is with English dialogue. The results of the research show that SimSimi in English can communicate more effectively and efficiently compared to its use in Arabic.
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Nuruzzaman, Mohammad, and Omar Khadeer Hussain. "IntelliBot: A Dialogue-based chatbot for the insurance industry." Knowledge-Based Systems 196 (May 2020): 105810. http://dx.doi.org/10.1016/j.knosys.2020.105810.
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Fatima. "A STUDY AND ANALYSIS ON CHATBOTS FOR BUSINESSES USING BOTSIFY." International Journal of Interpreting Enigma Engineers 01, no. 01 (2024): 01–09. http://dx.doi.org/10.62674/ijiee.2024.v1i01.001.
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A bot is a piece of software that executes different jobs and behaves like a human by following instructions. The acronym BOT stands for Build, Operate, Transfer. As a type of bot, chatbots can be thought of as conversational agents and have become extremely useful in the field of human-computer interaction. Advances in deep learning models, language understanding, and the availability of large datasets have led to notable progress in the development and implementation of chatbots. The article examines the essential elements and difficulties involved in developing chatbots. The study discusses a variety of chatbot training techniques, including rule-based systems and more intricate neural network architectures. Botsify is a platform for creating chatbots that also uses artificial intelligence (AI) to improve dialogue. With Botsify's revolutionary power, Chatter-Champion emerges as the prototype of conversational excellence, reaching new heights. Architecture gives board summary and basic implanting methods. The unique needs and interconnections with external systems may influence the actual implementations.
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Krisnawati, Lucia Dwi, Bill Edward Butar-Butar, and Gloria Virginia. "Prototyping a Chatbot for Student Supervision in a Pre-Registration Process." CommIT (Communication and Information Technology) Journal 12, no. 2 (October 31, 2018): 87. http://dx.doi.org/10.21512/commit.v12i2.4813.
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Developing a chatbot becomes a challenging task when it is built from scratch and independent of any Software as a Service (SaaS). Inspired by the idea of freeing lecturers from the burden of answering the same questions repetitively during the pre-registration process, this research has succeeded in building a textbased chatbot system. Further, this research has proved that the combination of keyword spotting technique for the Language Understanding component, Finite-State Transducer (FST) for the Dialogue Management, rulebased keyword matching for language generation, and the system-in-the-loop paradigm for system validation can produce an efficient chatbot. The chatbot efficiency is high enough as its score on Concept Efficiency (CE) reaches 0.946. It shows that users do not need to repeat their utterances several times to be understood. The chatbot performance on recognizing new concepts introduced by users is also more than satisfactory which is presented by its Query Density (QD) score of 0.80.
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Permatasari, Dinda Ayu, and Devira Anggi Maharani. "Combination of Natural Language Understanding and Reinforcement Learning for Booking Bot." Journal of Electrical, Electronic, Information, and Communication Technology 3, no. 1 (April 30, 2021): 12. http://dx.doi.org/10.20961/jeeict.3.1.49818.
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At present, some popular messaging applications have evolved specifically with bots starting to emerge into development. One of the developments of chatbots is to help humans booking flight with Named Entity Recognition in the text, trace sentences to detect user intentions, and respond even though the context of the conversation domain is limited. This study proposes to conduct analysis and design chatbot interactions using NLU (Natural Language Understanding) with the aim that the bot understands what is meant by the user and provides the best and right response. Classification using Support Vector Machine (SVM) method with (erm Frequency-Inverse Document Frequency (TF-IDF) feature extraction is suitable combination methods that produce the highest accuracy value up to 97.5%. Conversation dialogue on chatbots developed using NLU which consists of NER and intent classification then dialog manager using Reinforcement Learning could make a low cost for computing in chatbots.
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Kim, Jintae, Shinhyeok Oh, Oh-Woog Kwon, and Harksoo Kim. "Multi-Turn Chatbot Based on Query-Context Attentions and Dual Wasserstein Generative Adversarial Networks." Applied Sciences 9, no. 18 (September 18, 2019): 3908. http://dx.doi.org/10.3390/app9183908.
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To generate proper responses to user queries, multi-turn chatbot models should selectively consider dialogue histories. However, previous chatbot models have simply concatenated or averaged vector representations of all previous utterances without considering contextual importance. To mitigate this problem, we propose a multi-turn chatbot model in which previous utterances participate in response generation using different weights. The proposed model calculates the contextual importance of previous utterances by using an attention mechanism. In addition, we propose a training method that uses two types of Wasserstein generative adversarial networks to improve the quality of responses. In experiments with the DailyDialog dataset, the proposed model outperformed the previous state-of-the-art models based on various performance measures.
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M.V. Vijaya Saradhi, Swajan Reddy Gaddampally, Sai Kumar Chamarla, Arun Reddy Cheluveru, and Adityan Tamarapu. "Human Mimic Chatbot." World Journal of Advanced Research and Reviews 18, no. 3 (June 30, 2023): 1232–39. http://dx.doi.org/10.30574/wjarr.2023.18.3.1228.
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A Telegram bot that lets users enter a contact's name and a text file for a chat serves as the project's entry point. The bot turns the conversation data into a structured pandas dataframe after extracting it. The chatbot is then trained using the conversation data and the Microsoft Dialogue GPT model so that it can produce responses resembling those of the selected contact. The model is then deployed to the Hugging Face repository after training is finished, and users are given access to a run.py file. This Python programme interacts with WhatsApp using Selenium to keep track of new messages from the chosen contact. The chatbot concept is applied to construct an appropriate reply when a new message is received, automating the reply procedure. The project's benefits include improved productivity, tailored responses, and easy integration with well-liked messaging platforms. As the chatbot responds to incoming messages in line with the conversational style of the designated contact, users may now concentrate on their activities without the need for frequent manual engagement. The project does, however, have several drawbacks, such as its reliance on the reliability and accessibility of the offered conversation history. Future improvements might use sentiment analysis, context awareness, and advanced natural language processing techniques to overcome these restrictions and improve the effectiveness of the chatbot.
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Sysoyev, Pavel V., and Evgeny M. Filatov. "Chatbots in teaching a foreign language: advantages and controversial issues." Tambov University Review. Series: Humanities, no. 1 (2023): 66–72. http://dx.doi.org/10.20310/1810-0201-2023-28-1-66-72.
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The current stage of technological development of the world community is characterized by the dynamic introduction of artificial intelligence technologies into various spheres of human life. Informatization of education contributed to the integration of new innovative technologies in the teaching of certain subjects, including a foreign language. Chatbots are one of the modern programs that operate on the basis of natural language processing and machine learning technologies, which can be used in the development of students’ foreign language speech skills. By chatbots from the standpoint of the methodology of teaching foreign languages, we propose to understand a dialog training program capable of developing foreign-language oral and written speech skills of a student based on the algorithms of human speech behavior embedded in it by maintaining a dialogue with him and imitating human speech. In this work, based on the analysis of methodological research, we highlight the advantages of chatbots in teaching a foreign language and the controversial issues of using this program in the educational process. The advantages include the following: 1) increasing students' motivation to learn a foreign language based on innovative artificial intelligence technology; 2) the opportunity for students to improve foreign language speech skills; 3) the availability of chatbots for the development of students' speech skills, regardless of their location and time; 4) reducing the level of anxiety of students when interacting with the machine. The controversial issues include: 1) students are not always ready to replace the teacher with a chatbot; 2) most of the conversational chatbots are aimed at students with a level of foreign language proficiency A2-B1; 3) stereotyped and limited set of phrases used by the chatbot.
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Xu, Zhenyu, Hailin Xu, Zhouyang Lu, Yingying Zhao, Rui Zhu, Yujiang Wang, Mingzhi Dong, et al. "Can Large Language Models Be Good Companions?" Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 8, no. 2 (May 13, 2024): 1–41. http://dx.doi.org/10.1145/3659600.
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Developing chatbots as personal companions has long been a goal of artificial intelligence researchers. Recent advances in Large Language Models (LLMs) have delivered a practical solution for endowing chatbots with anthropomorphic language capabilities. However, it takes more than LLMs to enable chatbots that can act as companions. Humans use their understanding of individual personalities to drive conversations. Chatbots also require this capability to enable human-like companionship. They should act based on personalized, real-time, and time-evolving knowledge of their users. We define such essential knowledge as the common ground between chatbots and their users, and we propose to build a common-ground-aware dialogue system from an LLM-based module, named OS-1, to enable chatbot companionship. Hosted by eyewear, OS-1 can sense the visual and audio signals the user receives and extract real-time contextual semantics. Those semantics are categorized and recorded to formulate historical contexts from which the user's profile is distilled and evolves over time, i.e., OS-1 gradually learns about its user. OS-1 combines knowledge from real-time semantics, historical contexts, and user-specific profiles to produce a common-ground-aware prompt input into the LLM module. The LLM's output is converted to audio, spoken to the wearer when appropriate. We conduct laboratory and in-field studies to assess OS-1's ability to build common ground between the chatbot and its user. The technical feasibility and capabilities of the system are also evaluated. Our results show that by utilizing personal context, OS-1 progressively develops a better understanding of its users. This enhances user satisfaction and potentially leads to various personal service scenarios, such as emotional support and assistance.
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Boné, João, João C. Ferreira, Ricardo Ribeiro, and Gonçalo Cadete. "DisBot: A Portuguese Disaster Support Dynamic Knowledge Chatbot." Applied Sciences 10, no. 24 (December 18, 2020): 9082. http://dx.doi.org/10.3390/app10249082.
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This paper presents DisBot, the first Portuguese speaking chatbot that uses social media retrieved knowledge to support citizens and first-responders in disaster scenarios, in order to improve community resilience and decision-making. It was developed and tested using Design Science Research Methodology (DSRM), being progressively matured with field specialists through several design and development iterations. DisBot uses a state-of-the-art Dual Intent Entity Transformer (DIET) architecture to classify user intents, and makes use of several dialogue policies for managing user conversations, as well as storing relevant information to be used in further dialogue turns. To generate responses, it uses real-world safety knowledge, and infers a dynamic knowledge graph that is dynamically updated in real-time by a disaster-related knowledge extraction tool, presented in previous works. Through its development iterations, DisBot has been validated by field specialists, who have considered it to be a valuable asset in disaster management.
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Kasinathan, Vinothini, Aida Mustapha, and Chow Khai Bin. "A Customizable Multilingual Chatbot System for Customer Support." Annals of Emerging Technologies in Computing 5, no. 5 (March 20, 2021): 51–59. http://dx.doi.org/10.33166/aetic.2021.05.006.
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Implementing third-party services to develop a chatbot is not cost-effective for many small and medium enterprises especially considering that many services only supports one language at a time. To address this gap, this paper proposes a multilingual chatbot system that will allow companies and organizations to customize and deploy their own multilingual chatbot service with two extended features, which are live chat and ticketing system. The chatbot will also be able to understand and reply in English, Malay, and Chinese as well as customizable given the dialogue shell and knowledge base. To achieve this, the development uses TypeScript for frontend web application while Go as the backend development. The development language for mobile application is Dart and the User Interface (UI) library is React. Finally, the database management system used is MongoDB. The developed prototype is then evaluated via a survey questionnaire and the findings suggested that the proposed system would be able to assist small and medium-sized business and organizations to deploy their own chatbot system as an alternative to existing customer services.
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Kim, Jintae, Hyeon-gu Lee, and Harksoo Kim. "Effective Generative Chatbot Model Trainable with a Small Dialogue Corpus." Journal of KIISE 46, no. 3 (March 31, 2019): 246–52. http://dx.doi.org/10.5626/jok.2019.46.3.246.
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Chow, James C. L., Leslie Sanders, and Kay Li. "Design of an Educational Chatbot Using Artificial Intelligence in Radiotherapy." AI 4, no. 1 (March 2, 2023): 319–32. http://dx.doi.org/10.3390/ai4010015.
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Context: In cancer centres and hospitals particularly during the pandemic, there was a great demand for information, which could hardly be handled by the limited manpower available. This necessitated the development of an educational chatbot to disseminate topics in radiotherapy customized for various user groups, such as patients and their families, the general public and radiation staff. Objective: In response to the clinical demands, the objective of this work is to explore how to design a chatbot for educational purposes in radiotherapy using artificial intelligence. Methods: The chatbot is designed using the dialogue tree and layered structure, incorporated with artificial intelligence features such as natural language processing (NLP). This chatbot can be created in most platforms such as the IBM Watson Assistant and deposited in a website or various social media. Results: Based on the question-and-answer approach, the chatbot can provide humanlike communication to users requesting information on radiotherapy. At times, the user, often worried, may not be able to pinpoint the question exactly. Thus, the chatbot will be user friendly and reassuring, offering a list of questions for the user to select. The NLP system helps the chatbot to predict the intent of the user so as to provide the most accurate and precise response to him or her. It is found that the preferred educational features in a chatbot are functional features such as mathematical operations, which should be updated and modified routinely to provide new contents and features. Conclusions: It is concluded that an educational chatbot can be created using artificial intelligence to provide information transfer to users with different backgrounds in radiotherapy. In addition, testing and evaluating the performance of the chatbot is important, in response to user’s feedback to further upgrade and fine-tune the chatbot.
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Puspitasari, Agreianti, Yeni Fajariyanti, Myrtana Pusparisti, Agustin Amborowati, and Muhammad Tafdhil. "Artificial Intelligence in Chatbot Website Platform." Jurnal Penelitian Pendidikan IPA 9, no. 12 (December 20, 2023): 11145–50. http://dx.doi.org/10.29303/jppipa.v9i12.5580.
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In computing discourse, a Chatbot is a computer programme that is specifically designed to simulate an interactive communication or conversation. The interactive communication is from the machine to the user (human) either through text media, sound media or visual media. Chatbots have been widely used for practical purposes such as online assistance, personalised services, or information acquisition, including in the world of global marketing. The purpose of this research is to describe how a chatbot platform provides its best function in supporting a marketing task of a corporation at a global level. The methods used in this research are: 1) System Analysis, which is the collection of information needed in building the system must be done in detail. Where this information will support all the components needed to obtain results that are in accordance with all the needs related to the design of the system to be input, 2) Preparation of flowcharts, namely the design by entering data on the status of conversations that are commonly carried out by the Help-Desk with customers. Where when the user enters a word or sentence in the column that is already available in the system, a word or sentence search process will be carried out based on the noun, this process is useful for matching whether the input given by the user is in the set of nouns that have been trained in dialogue flow. The result obtained is that the website becomes one of the company's main media in marketing its products because the website already includes all information related to the product and also related to the company. However, when using the website alone, there is no direct communication with potential buyers. Therefore, this research will develop a chatbot that can improve the performance of the Spicering ltd website.
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Dr. Su. Suganthi, Mr. Nithish Kumar R, Mr. Logeswaran S R, Mr. Rohan Kumar M, and Mr. Hariprasad V. "AI Chaperone: Awareness Chatbot for Alzheimer’s Disease." International Research Journal on Advanced Engineering and Management (IRJAEM) 2, no. 05 (May 13, 2024): 1245–49. http://dx.doi.org/10.47392/irjaem.2024.0168.
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This project proposes the development of a RAG Chatbot tailored specifically to assist Alzheimer's patients in managing their memory abnormalities and providing them with answers to their queries. The chatbot utilizes state-of-the-art natural language processing techniques to understand and respond to patient inquiries, while leveraging RAG to enhance the relevance and accuracy of its responses.The primary goal of this project is to provide Alzheimer's patients with a supportive and interactive tool that can help mitigate the impact of memory impairment on their daily lives. It acts as an information resource, offering answers to common questions about Alzheimer's disease, treatment options, and lifestyle management strategies.Key features of the RAG Chatbot include personalized conversation histories to track patient interactions and preferences, adaptive dialogue generation to tailor responses to individual needs, and integration with existing healthcare systems for seamless coordination of care. Furthermore, the chatbot undergoes continuous improvement through machine learning algorithms that analyze patient feedback and update its knowledge base accordingly. Overall, the RAG Chatbot represents a promising advancement in Alzheimer's patient assistance, offering a scalable and accessible solution to support individuals living with memory disorders.
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Lin, Zhaojiang, Peng Xu, Genta Indra Winata, Farhad Bin Siddique, Zihan Liu, Jamin Shin, and Pascale Fung. "CAiRE: An End-to-End Empathetic Chatbot." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 09 (April 3, 2020): 13622–23. http://dx.doi.org/10.1609/aaai.v34i09.7098.
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We present CAiRE, an end-to-end generative empathetic chatbot designed to recognize user emotions and respond in an empathetic manner. Our system adapts the Generative Pre-trained Transformer (GPT) to empathetic response generation task via transfer learning. CAiRE is built primarily to focus on empathy integration in fully data-driven generative dialogue systems. We create a web-based user interface which allows multiple users to asynchronously chat with CAiRE. CAiRE also collects user feedback and continues to improve its response quality by discarding undesirable generations via active learning and negative training.