Academic literature on the topic 'Chatbot dialog'
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Journal articles on the topic "Chatbot dialog"
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Le, Nhat, A. B. Siddique, Fuad Jamour, Samet Oymak, and Vagelis Hristidis. "Generating Predictable and Adaptive Dialog Policies in Single- and Multi-domain Goal-oriented Dialog Systems." International Journal of Semantic Computing 15, no. 04 (December 2021): 419–39. http://dx.doi.org/10.1142/s1793351x21400109.
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Most existing commercial goal-oriented chatbots are diagram-based; i.e. they follow a rigid dialog flow to fill the slot values needed to achieve a user’s goal. Diagram-based chatbots are predictable, thus their adoption in commercial settings; however, their lack of flexibility may cause many users to leave the conversation before achieving their goal. On the other hand, state-of-the-art research chatbots use Reinforcement Learning (RL) to generate flexible dialog policies. However, such chatbots can be unpredictable, may violate the intended business constraints, and require large training datasets to produce a mature policy. We propose a framework that achieves a middle ground between the diagram-based and RL-based chatbots: we constrain the space of possible chatbot responses using a novel structure, the chatbot dependency graph, and use RL to dynamically select the best valid responses. Dependency graphs are directed graphs that conveniently express a chatbot’s logic by defining the dependencies among slots: all valid dialog flows are encapsulated in one dependency graph. Our experiments in both single-domain and multi-domain settings show that our framework quickly adapts to user characteristics and achieves up to 23.77% improved success rate compared to a state-of-the-art RL model.
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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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Suh, Jeehae. "A Study on the Conformity of Chatbot Builder as a Korean Speech Practice Tool." Korean Society of Culture and Convergence 45, no. 1 (January 31, 2023): 61–70. http://dx.doi.org/10.33645/cnc.2023.01.45.01.61.
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The purpose of this study is to verify whether chatbots made with chatbot builders are suitable as a Korean speaking practice tool. Chatbot builders, which can be easily produced as chatbots without separate coding knowledge and can design conversations meaningful for learning, have recently been in the spotlight as a learning tool. In this study, chatbots were created using dialog flows, and conversation patterns with chatbots shared by study participants were analyzed. As a result of the analysis, it was found that 35% of all conversations were not successfully completed. Such a conversation failure was found to be due to the inaccuracy of chatbot's recognition of Korean learner pronunciation, error in handling learner utterance intention, and inaccuracy in handling learner error sentences. In this regard, in order for chatbot builder to be used as a Korean language learning tool now, learner's proficiency or academic achievement should be considered for smooth processing of chatbot's learner utterance. In addition, this study is meaningful in that it verified the suitability of chatbot builders as a learning tool not covered in previous studies.
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Dehankar, Archana, Shyamal Kirpan, Abhayraj Jha, Gitesh Thosare, Adarsh Bhaisare, and Sumit Mankar. "AI CHATBOT USING DIALOG FLOW." International Journal of Computer Science and Mobile Computing 11, no. 5 (May 30, 2022): 68–74. http://dx.doi.org/10.47760/ijcsmc.2022.v11i05.006.
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A chatbot is a programme that uses Artificial Intelligence to provide human conversation (AI). Chatbots are intended to serve as VIRTUAL ASSISTANTS (VA). They themselves provide one platform for the online promotion of Products and Services. All Higher Educational Institutes provide comprehensive information to students via their websites, which permit the use of social networks such as Facebook, WhatsApp, but also College websites. Overall, searching functionality is required in any website to search for any information, and it includes Social Networking sites Applications such as Facebook and Snapchat regular response are utilised. As a result, Chatbot is both an effective auto-response system and an instant messaging platform. In this paper, AICMS, an AI-based CollegeBot management platform for professional Engineering colleges, provides auto-response to college queries about the college's basic information, class schedules, and academic examination schedules. The system can handle a large number of questions about subjects and placements. The AICMS system is built with Dialogflow, which is supported by the Google API. AI is running as a messenger on Facebook, taking input in the form of text and voice and responding in the form of text and voice. It responds quickly and accurately to student and staff inquiries in an interactive manner.
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Dehankar, Archana, Shyamal Kirpan, Abhayraj Jha, Gitesh Thosare, Adarsh Bhaisare, and Sumit Mankar. "AI CHATBOT USING DIALOG FLOW." International Journal of Computer Science and Mobile Computing 11, no. 5 (May 30, 2022): 68–74. http://dx.doi.org/10.47760/ijcsmc.2022.v11i05.006.
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A chatbot is a programme that uses Artificial Intelligence to provide human conversation (AI). Chatbots are intended to serve as VIRTUAL ASSISTANTS (VA). They themselves provide one platform for the online promotion of Products and Services. All Higher Educational Institutes provide comprehensive information to students via their websites, which permit the use of social networks such as Facebook, WhatsApp, but also College websites. Overall, searching functionality is required in any website to search for any information, and it includes Social Networking sites Applications such as Facebook and Snapchat regular response are utilised. As a result, Chatbot is both an effective auto-response system and an instant messaging platform. In this paper, AICMS, an AI-based CollegeBot management platform for professional Engineering colleges, provides auto-response to college queries about the college's basic information, class schedules, and academic examination schedules. The system can handle a large number of questions about subjects and placements. The AICMS system is built with Dialogflow, which is supported by the Google API. AI is running as a messenger on Facebook, taking input in the form of text and voice and responding in the form of text and voice. It responds quickly and accurately to student and staff inquiries in an interactive manner.
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Zand, Aria, Arjun Sharma, Zack Stokes, Courtney Reynolds, Alberto Montilla, Jenny Sauk, and Daniel Hommes. "An Exploration Into the Use of a Chatbot for Patients With Inflammatory Bowel Diseases: Retrospective Cohort Study." Journal of Medical Internet Research 22, no. 5 (May 26, 2020): e15589. http://dx.doi.org/10.2196/15589.
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Background The emergence of chatbots in health care is fast approaching. Data on the feasibility of chatbots for chronic disease management are scarce. Objective This study aimed to explore the feasibility of utilizing natural language processing (NLP) for the categorization of electronic dialog data of patients with inflammatory bowel diseases (IBD) for use in the development of a chatbot. Methods Electronic dialog data collected between 2013 and 2018 from a care management platform (UCLA eIBD) at a tertiary referral center for IBD at the University of California, Los Angeles, were used. Part of the data was manually reviewed, and an algorithm for categorization was created. The algorithm categorized all relevant dialogs into a set number of categories using NLP. In addition, 3 independent physicians evaluated the appropriateness of the categorization. Results A total of 16,453 lines of dialog were collected and analyzed. We categorized 8324 messages from 424 patients into seven categories. As there was an overlap in these categories, their frequencies were measured independently as symptoms (2033/6193, 32.83%), medications (2397/6193, 38.70%), appointments (1518/6193, 24.51%), laboratory investigations (2106/6193, 34.01%), finance or insurance (447/6193, 7.22%), communications (2161/6193, 34.89%), procedures (617/6193, 9.96%), and miscellaneous (624/6193, 10.08%). Furthermore, in 95.0% (285/300) of cases, there were minor or no differences in categorization between the algorithm and the three independent physicians. Conclusions With increased adaptation of electronic health technologies, chatbots could have great potential in interacting with patients, collecting data, and increasing efficiency. Our categorization showcases the feasibility of using NLP in large amounts of electronic dialog for the development of a chatbot algorithm. Chatbots could allow for the monitoring of patients beyond consultations and potentially empower and educate patients and improve clinical outcomes.
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Bogdanova, A. N., and G. A. Fedorova. "Chatbots as a component of the content of teaching the basics of artificial intelligence at school." Informatics in school, no. 2 (July 17, 2022): 39–45. http://dx.doi.org/10.32517/2221-1993-2022-21-2-39-45.
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The article discusses the relevance of teaching schoolchildren the basics of artifcial intelligence. The importance of chatbots is emphasized as a component of the content of the theme "Fundamentals of artifcial intelligence" for the development of interdisciplinary links of the school course of informatics with other areas of knowledge. The theoretical aspects of teaching schoolchildren the technology of creating chatbots are considered: the characteristics of the basic concepts, historical information, the most common classifcation, the mechanism for processing user requests by the chatbot. Guidelines for teaching the technology of creating a chatbot are proposed. The designers for creating chatbots available for development by schoolchildren are listed. A plan of practical work "Creating a chatbot in the Aimylogic online constructor" is given. In order to illustrate the results of approbation of the educational material under consideration, examples of chatbots are given: a chatbot that talks about various types of algebraic equations; a chatbot — assistant for fractions; a chatbot — consultant on the theme "Algorithm and its properties. Ways of algorithm representation". The presented examples include the structure of the chatbot dialog created in the Aimylogic constructor and in the Pencil Code service.
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Chen, Jenhui, Obinna Agbodike, and Lei Wang. "Memory-Based Deep Neural Attention (mDNA) for Cognitive Multi-Turn Response Retrieval in Task-Oriented Chatbots." Applied Sciences 10, no. 17 (August 22, 2020): 5819. http://dx.doi.org/10.3390/app10175819.
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One of the important criteria used in judging the performance of a chatbot is the ability to provide meaningful and informative responses that correspond with the context of a user’s utterance. Nowadays, the number of enterprises adopting and relying on task-oriented chatbots for profit is increasing. Dialog errors and inappropriate response to user queries by chatbots can result in huge cost implications. To achieve high performance, recent AI chatbot models are increasingly adopting the Transformer positional encoding and the attention-based architecture. While the transformer performs optimally in sequential generative chatbot models, recent studies has pointed out the occurrence of logical inconsistency and fuzzy error problems when the Transformer technique is adopted in retrieval-based chatbot models. Our investigation discovers that the encountered errors are caused by information losses. Therefore, in this paper, we address this problem by augmenting the Transformer-based retrieval chatbot architecture with a memory-based deep neural attention (mDNA) model by using an approach similar to late data fusion. The mDNA is a simple encoder-decoder neural architecture that comprises of bidirectional long short-term memory (Bi-LSTM), attention mechanism, and a memory for information retention in the encoder. In our experiments, we trained the model extensively on a large Ubuntu dialog corpus, and the results from recall evaluation scores show that the mDNA augmentation approach slightly outperforms selected state-of-the-art retrieval chatbot models. The results from the mDNA augmentation approach are quite impressive.
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Wang, Youwei. "Discover two Neural Machine Translation model variables' effects on Chatbot's performance." Highlights in Science, Engineering and Technology 41 (March 30, 2023): 17–22. http://dx.doi.org/10.54097/hset.v41i.6737.
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To offer automatic online advice and help, chatbots are sophisticated conversational computer systems that resemble a human conversation. As chatbots' advantages grew, a variety of sectors began to use them extensively to give customers virtual support. Chatbots take advantage of methods and algorithms from two Artificial Intelligence areas: Machine Learning and Natural Language Processing. There are still several obstacles and restrictions to their use. In order to discover two NMT model variables' effects on chatbot performance, this paper does several experiments on a deep neural network chatbot model. Two straightforward and useful kinds of attentional mechanisms are used in this chatbot model: a local technique that only considers a small subset of source words at a time, as opposed to a global approach that always pays attention to all source words. This paper conducts experiments to examine how different model variables affect chatbot performance. This paper created a question template with eight general questions to test chatbot performance. Through the whole experiment results, increasing the number of iterations and increasing the dataset scale can improve the vocabulary and logic of the chatbot dialog to achieve better performance.
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Reddy, Mr P. Rajashekar. "Enhancing User Experience: A Study on Dialogflow Chatbot Implementation." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (March 7, 2024): 1–11. http://dx.doi.org/10.55041/ijsrem29070.
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In recent years, chatbots have become essential for customer service, task automation, and information dissemination. Google's Dialogflow platform offers intuitive tools for building conversational agents. This paper provides an overview of developing smart chatbots with Dialogflow, emphasizing natural language understanding. It covers Dialogflow's architecture, including intents, entities, and fulfillment, and discusses training techniques for chatbot optimization. Additionally, it explores advanced features like external service integration and multilingual support. Overall, it serves as a practical guide for developers and organizations interested in leveraging Dialogflow to create personalized chatbot experiences. Keywords : User input, Conversation flow, Dialog management, User experience (UX)
Dissertations / Theses on the topic "Chatbot dialog"
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Kero, Chanelle, and Veronica Törnblom. "Utveckling av en chatbots dialog för implementation i en webbaserad kundtjänst." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-69046.
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Automatisering av kundtjänster genom implementering av chatbotar blir allt vanligare för att kunna erbjuda kunder en mer tillgänglig och effektiv service. Vid implementering av chatbotar i kundtjänster lämnas den mänskliga kontrollen och kundens upplevelse delvis över till ett digitalt system, vilket ställer krav på utformning av chatbotars dialoger. Syftet med arbetet är att ta fram en artefakt i form av en chatbot och utveckla dess dialog för en framtida implementation i en kundtjänst. Arbetet ämnar att ta fram designprinciper för utformning och utveckling av chatbotar och deras dialoger i en kundtjänstmiljö. Den forskningsmetod som tillämpades vid utvecklingen av artefakten var Design Science Research Methodology (DSRM). En litteraturstudie genomfördes och data samlades in genom möten samt en intervju med det företag som varit uppdragsgivare till arbetet. Arbetet resulterade i en demosida innehållande en chatbot, samt en sida för administratörer där de har en översikt av chatbotens genomförda interaktioner. Tre designprinciper togs även fram för utveckling av chatbotars dialog inom kundtjänst, vilka blev arbetets bidrag. De slutsatser som identifierades var att en chatbot bör kontrollera att användaren fått svar på sitt kundtjänstärende, en chatbot bör erbjuda mänsklig service vid missförstånd, en chatbot kan inte ersätta mänsklig service fullt ut samt att en chatbot bör ha en strukturerad datainsamling av genomförda interaktioner för att kunna utveckla och förbättra chatbotens dialog.Automation of customer service through implementation of chatbots is becoming increasingly common in order to provide customers with a more accessible and efficient service. When implementing chatbots in customer services, human control and the customer experience are partially shifted to a digital system, which puts requirement on the chatbot’s dialogue. The purpose of this report is to develop an artifact for a client. The artifact is a web page containing a chatbot. The report aims to develop design principles for development of chatbots and their dialogues in a customer service. The research method used in the development of the artifact was Design Science Research Methodology (DSRM). A literature study was conducted, and data was collected through meetings and an interview with the client. The report resulted in a demo page containing a chatbot and a page for administrators with an overview of saved chats. Three design principles were also formulated for developing chatbots in customer service, which is the contribution of the report. The conclusions identified were that a chatbot should check that the user got the correct answer, a chatbot should offer human service in case of misunderstanding, a chatbot cannot fully replace human service and a chatbot should have a structured data collection of completed interactions for developing and improvement of the chatbot’s dialogue.
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Lipecki, Johan, and Viggo Lundén. "The Effect of Data Quantity on Dialog System Input Classification Models." Thesis, KTH, Hälsoinformatik och logistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-237282.
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This paper researches how different amounts of data affect different word vector models for classification of dialog system user input. A hypothesis is tested that there is a data threshold for dense vector models to reach the state-of-the-art performance that have been shown with recent research, and that character-level n-gram word-vector classifiers are especially suited for Swedish classifiers–because of compounding and the character-level n-gram model ability to vectorize out-of-vocabulary words. Also, a second hypothesis is put forward that models trained with single statements are more suitable for chat user input classification than models trained with full conversations. The results are not able to support neither of our hypotheses but show that sparse vector models perform very well on the binary classification tasks used. Further, the results show that 799,544 words of data is insufficient for training dense vector models but that training the models with full conversations is sufficient for single statement classification as the single-statement- trained models do not show any improvement in classifying single statements.Detta arbete undersöker hur olika datamängder påverkar olika slags ordvektormodeller för klassificering av indata till dialogsystem. Hypotesen att det finns ett tröskelvärde för träningsdatamängden där täta ordvektormodeller när den högsta moderna utvecklingsnivån samt att n-gram-ordvektor-klassificerare med bokstavs-noggrannhet lämpar sig särskilt väl för svenska klassificerare söks bevisas med stöd i att sammansättningar är särskilt produktiva i svenskan och att bokstavs-noggrannhet i modellerna gör att tidigare osedda ord kan klassificeras. Dessutom utvärderas hypotesen att klassificerare som tränas med enkla påståenden är bättre lämpade att klassificera indata i chattkonversationer än klassificerare som tränats med hela chattkonversationer. Resultaten stödjer ingendera hypotes utan visar istället att glesa vektormodeller presterar väldigt väl i de genomförda klassificeringstesterna. Utöver detta visar resultaten att datamängden 799 544 ord inte räcker till för att träna täta ordvektormodeller väl men att konversationer räcker gott och väl för att träna modeller för klassificering av frågor och påståenden i chattkonversationer, detta eftersom de modeller som tränats med användarindata, påstående för påstående, snarare än hela chattkonversationer, inte resulterar i bättre klassificerare för chattpåståenden.
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Gligorijevic, Ilic Nemanja. "Utveckling av en FAQ chatbot - för frågor om ett program på ett universitet." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-85197.
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The implementation of chatbots and service services is becoming more common. The reason for this is that they are constantly available to answer questions, no matter what time it is. To make one chatbot is not just to write questions and specific answers. Communicating with one chatbot should, as much as possible, look like communicating with another human. The purpose of this report is to create a chatbot that will be used at Luleå university of technology and that will answer questions about system science. Furthermore, the goal is to investigate which design principles should be implemented, their possible concretization, when making chatbots, and possibly coming to new principles. The method used to make the chatbot is Design Science Research Methodology (DSRM). DSRM focuses on solving the problem by creating an IT artifact, which in this case is a chatbot. The result of this work is a created chatbot and design principles that were implemented during the development process.
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Asher, Natali. "A Warmer Welcome : Application of a Chatbot as a Facilitator for New Hires Onboarding." Thesis, Linnéuniversitetet, Institutionen för medieteknik (ME), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-65887.
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Despite being explored and constantly improved through the years, onboarding of new hires in corporate organizations has remained a challenge. Many of the issues can be linked to a lack of communication between the organization and the new employee, as well as the common nature of these environments where information is spread across job titles and sources. This thesis discusses the feasibility of implementing a basic chatbot that will allow new hires to ask questions and request varied information at all times, using an interface such as a messaging app. This research explores the way chatbots should be designed in order to be effective, reliable and enjoyable from a user experience perspective. The chatbot was developed using the Chatfuel platform and tested by new employees at a corporate environment. The users were requested to explore the chatbot freely and then asked to answer a survey. The interactions were also recorded and analyzed from in both qualitative and quantitative ways (chat logs and analytics). The study proves that an onboarding chatbot is a useful tool for new hires and can be used as a communication facilitator between the organization and the new hires during the first weeks of employment, and also after that, serving as an information source and a broadcasting method. The chatbot gives the new hires an accessible source of information that helps on the process of getting to speed, and enables a positive experience that increases familiarity in the new workplace.
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Lavista, Andrea. "Natural language processing: chatbot per gli studenti del Campus di Cesena." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19555/.
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Il natural language processing è un campo di ricerca interdisciplinare che spazia tra informatica, linguistica e intelligenza artificiale, che si pone come obiettivo il trattamento automatico del linguaggio naturale, ovvero l’analisi, la comprensione e l’elaborazione del linguaggio da parte dei calcolatori. Uno dei problemi più interessanti e ancora aperti di questa disciplina è la capacità di dialogo per la quale ricercatori e studiosi stanno ideando e impiegando nuovi approcci, in particolare basati su reti neurali artificiali, raggiungendo risultati sempre migliori. In questa tesi, oltre a dare una panoramica dello stato dell'arte, si espone lo sviluppo di un chatbot in contesto universitario, realizzato con il framework Rasa. Esso è pensato per gli studenti del Campus di Cesena e fornisce informazioni relative alla vita universitaria, principalmente sui corsi di laurea e sui bandi di Ateneo.
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Westin, Anna. "Different recipient designs with dialogue partners : An experimental comparison between a Chatbot and a Human communication partner." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-151886.
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Chatbots are becoming more common in modern society, but there are almost no studies that explore both the differences and causes that divides human communication from communication with a chatbot. The aim of this thesis was to explore different recipient design people take when communicating with a human and a chatbot. A chatbot was built and an experiment was conducted that measured the performance and experience of the participants. A thematic analysis then found out causes for these experiences. The study focused on finding new differences in addition to exploring people’s boredom, frustration, understanding, repetition, and performance in a task. The study found differences and causes in people’s recipient design when communicating with a human compared to a chatbot, as well as differences in the performance of a task. Hopefully, this will help future research figure out solutions for the differences found.
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Fornander, Linnea. "Språklig anpassning till en artificiell dialogpartner." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139246.
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Trots att antalet medier via vilka vi människor kan kommunicera med varandra är högre än någonsin, är talad interaktion något unikt betydelsefullt för våra mänskliga samhällen och relationer och därför ett ständigt relevant forskningsområde. Denna studie undersökte kvantitativa skillnader i hur människor konverserar med en talbaserad chattbot jämfört med en mänsklig konversationspartner, då de utförde en uppgift utformad för att skapa jämförliga dialoger. Resultatet visade på att konversationerna med chattbotten var mindre effektiva och ledde till sämre prestation i uppgiften. Dessutom påvisades att deltagarna använde en kortare genomsnittlig turlängd i konversationer med chattbotten, samt signifikanta skillnader i ordvariation mellan de två betingelserna. Dessa skillnader kan bero på att människor anpassar sitt språk efter sina uppfattningar av chattbottens kommunikativa egenskaper, och innebär att möjligheten att generalisera egenskaper hos människors tal i konversationer med liknande artificiella dialogpartners till konversationer med människor kan vara begränsad.
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Jakúbek, Tomáš. "Chatbot k aplikácii MojiLidi." Master's thesis, 2019. http://www.nusl.cz/ntk/nusl-429093.
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This diploma thesis deals with building a chatbot for the MojiLidi system, which helps to find specialists and companies providing their services and products. Chatbot is a type of interface that is controlled by a natural language conversation. Thus, users do not need to know the meaning of buttons or forms and for them it is enough just to say request. The work describes the main parts of chatbot such as understanding the natural language, managing the dialogue, creating responses, communication channel and the conversational user interface. Consequently, such a conversation agent is created and integrated into the MojiLidi web interface from where it helps users in 4 domains.
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Tai, André Gilberto. "PALbot: a Plug&(Almost)pLay chatbot." Master's thesis, 2019. http://hdl.handle.net/10071/19933.
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Para a comunicação entre seres humanos e computadores é necessária a identificação dos traços que os distinguem e das características do funcionamento de cada um. Desde a eletrónica, que é desenvolvida através de fenómenos físicos bem delineados, à informática, que assenta em axiomáticas matemáticas, o processo de funcionamento de um computador está livre de traços humanos e é conhecido por fornecer respostas concretas, verdadeiras e lógicas.
Neste projeto foi desenvolvido um sistema de diálogo, denominado por PALbot com o objetivo de melhorar a acessibilidade na procura de informação para os utilizadores da plataforma do Ciberdúvidas. O PALbot é um programa que simula uma conversa humana, esclarecendo dúvidas relativamente à Língua Portuguesa, na forma de diálogo com o utilizador. Após desenvolvimento do chatbot, foi realizado um inquérito de forma a avaliar a ferramenta desenvolvida. Os resultados do inquérito revelam 65,54% de respostas corretas com a base de conhecimento da plataforma do Ciberdúvidas inserida no sistema de conversação.
Adicionalmente, elaborou-se um conjunto de testes de forma a avaliar os atributos de qualidade, nomeadamente eficácia e eficiência, concluindo que 65,28% dos inquiridos concordam que o PALbot facilita quase sempre ou sempre o acesso à informação.
Em suma, o chabot desenvolvido durante a presente dissertação apresenta potencial para melhorar a acessibilidade dos utilizadores na procura de informação bem como para auxiliar a plataforma Ciberdúvidas melhorando o esclarecimento de dúvidas relativas à Língua Portuguesa.A good interaction between humans and computers is built on top of each other’s characteristics, functionality and behavior. From electronics, developed through actual physical phenomena, to informatics, built on mathematical axioms, the functionality of a computer is free of human trace. While computers are known for providing correct, true and logic answers, humans spend a vast amount of time on an informal dialog with almost no purpose. In this project, a dialog system named PALbot was developed to help Ciberdúvidas platform users access information. This program simulates a human conversation answering Portuguese linguistic questions to the user. After developing the chatbot, an inquire was made to evaluate the tool in question showing the program gave a correct answer 65.54% of times with the knowledge base of Ciberdúvidas platform. Additionally, another pool of tests was made to evaluate quality attributes such as efficiency and efficacy, in which was concluded that 65.28% of the enquired agree that PALbot always and almost always makes it easy to access to the information required. The chatbot developed during the current dissertation shows high potential to improve user’s accessibility to information as well as to help Ciberdúvidas platform answering Portuguese linguistic questions.
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Santos, Carlos Henrique Fernandes dos. "LEXIA: Commands Engine." Master's thesis, 2019. http://hdl.handle.net/10316/86365.
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Dissertação de Mestrado em Engenharia Informática apresentada à Faculdade de Ciências e TecnologiaAtualmente a quantidade e facilidade de acesso a informação é estonteante. Em meros momentos, uma pessoa pode-se manter atualizada sobre quaisquer tópicos de interesse. Esta facilidade de acesso mudou a perspetiva das pessoas e agora não só é a qualidade de informação disponibilizada que é valorizada, como também a velocidade a que é fornecida.Além disso, hoje em dia as pessoas tem expectativas de que informação e serviços sejam fornecidos de uma maneira fácil e intuitiva. Uma maneira de responder às expectativas das pessoas é através de assistentes virtuais. Os chatbots fornecem informação em tempo real e as interações com eles são feitas de uma forma fácil e intuitiva -- através de linguagem natural.Com a evolução das necessidades das empresas e das pessoas, a Critical Software (CSW) optou apoiar a criação de uma plataforma -- Lexia -- para o desenvolvimento de assistentes virtuais que possam interagir em vários domínios/contextos diferentes (por exemplo: podem agir como assistentes num banco ou como assistente para reservar quartos de hotel).Esta dissertação apresenta a primeira versão do Lexia. Esta primeira versão é capaz de dialogar com utilizadores e de ser implementada em vários contextos (e de executar tarefas dentro desses contextos). Usando a CSW como um caso de estudo, um agente conversacional que consegue interagir com sistemas internos desta empresa foi implementado.A versão do Lexia desenvolvida serve de fundação para possível trabalho futuro ser desenvolvido. Uma das maneiras possíveis de construir sobre esta versão é adicionando capacidades cognitivas ao sistema.
We live days in which the quantity of and rapid access to information available is staggering. In moments, a person may keep him or herself updated on topics of interest. This ease of access to information has changed people's perspectives and now, not only is the quality of information provided valued, but also the speed at which it is provided.Adding to the previous point, nowadays people expect information and services to be provided in a easy and intuitive way. A way to address the aforementioned problems is through the use of virtual assistants. Virtual assistants provide information in real-time and are interacted in a simple, intuitive way -- through natural language.With the evolution of people's and companies' needs, Critical Software (CSW) decided to go forward with the creation of a platform -- Lexia -- for developing virtual assistants that can be deployed (and execute tasks) in various different contexts/domains (e.g. deployed as an assistant in a bank or as an assistant to book hotels). This dissertation presents the first version of Lexia. This first version is capable of dialoguing with users and be deployed within various contexts (and execute tasks within those domains). Using CSW as a case study, a conversation engine that can interact with CSW's internal systems was deployed.The Lexia version developed in this internship lays the groundwork for future work to be developed. A possible way to build upon this version is to add cognitive capabilities to the platform.
Books on the topic "Chatbot dialog"
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Beaven, Tita, and Fernando Rosell-Aguilar, eds. Innovative language pedagogy report. Research-publishing.net, 2021. http://dx.doi.org/10.14705/rpnet.2021.50.9782490057863.
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The Innovative Language Pedagogy Report presents new and emerging approaches to language teaching, learning, and assessment in school, further education, and higher education settings. Researchers and practitioners provide 22 research-informed, short articles on their chosen pedagogy, with examples and resources. The report is jargon-free, written in a readable format, and covers, among others, gamification, open badges, comparative judgement, translanguaging, translation, learning without a teacher, and dialogue facilitation. It also includes technologies such as chatbots, augmented reality, automatic speech recognition, digital corpora, and LMOOCs, as well as pedagogical innovations around virtual exchange, digital storytelling, technology-facilitated oral homework, and TeachMeets.
Book chapters on the topic "Chatbot dialog"
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Baez, Marcos, Florian Daniel, and Fabio Casati. "Conversational Web Interaction: Proposal of a Dialog-Based Natural Language Interaction Paradigm for the Web." In Chatbot Research and Design, 94–110. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39540-7_7.
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Ali, Basit, and Vadlamani Ravi. "Developing Dialog Manager in Chatbots via Hybrid Deep Learning Architectures." In Advances in Intelligent Systems and Computing, 301–10. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5679-1_28.
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Vadász, Noémi. "Resolving Hungarian Anaphora with ChatGPT." In Text, Speech, and Dialogue, 45–57. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-40498-6_5.
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Fergencs, Tamás, and Florian Meier. "Engagement and Usability of Conversational Search – A Study of a Medical Resource Center Chatbot." In Diversity, Divergence, Dialogue, 328–45. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71292-1_26.
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Callejas-Rodríguez, Ángel, Esaú Villatoro-Tello, Ivan Meza, and Gabriela Ramírez-de-la-Rosa. "From Dialogue Corpora to Dialogue Systems: Generating a Chatbot with Teenager Personality for Preventing Cyber-Pedophilia." In Text, Speech, and Dialogue, 531–39. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45510-5_61.
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Hamad, Omama, Ali Hamdi, and Khaled Shaban. "Empathy and Persona of English vs. Arabic Chatbots: A Survey and Future Directions." In Text, Speech, and Dialogue, 525–37. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16270-1_43.
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Gotthardt, Marie, Julian Striegl, Claudia Loitsch, and Gerhard Weber. "Voice Assistant-Based CBT for Depression in Students: Effects of Empathy-Driven Dialog Management." In Lecture Notes in Computer Science, 451–61. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08648-9_52.
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AbstractWith a rising number of students with depression, new low-threshold solutions have to be found to strengthen the resilience against and help those affected by mental disorders. One approach lies in the usage of chatbots (CBs) to provide tools based in cognitive behavioral therapy (CBT) that can be used independently in order to reduce symptoms of depression. To ensure the adherence to such systems, a good usability and acceptance is important. Conversational agents (CAs) that provide CBT-based content should further be sensitive to the users emotional state, as empathy is one central aspect of therapy. While promising research has been going on in the field of CB-based empathy-driven CBT, voice assistant-based (VA-based) solutions have thus far not been investigated deeply. Therefore, we propose a VA-based, empathy-driven system, capable of delivering selected methods from CBT to students with depression.To assess the effects of empathy-driven dialog management on perceived usability and acceptance, we conducted a single blind randomized controlled A/B testing experiment with 10 participants. While the application of empathetical dialog management shows no benefits to the usability and acceptance, results overall indicate a good usability and acceptance of the system in the target group.
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Sajadieh, Sahar, and Hannen Wolfe. "Designing Interrogative Robot Theater: A Robot Who Won’t Take No for an Answer." In Creating Digitally, 183–211. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-31360-8_7.
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Abstract“You are the hottest thing in the room! I couldn’t help but come over to introduce myself.” That’s how the conversation began between the human-sized female robot and an audience member in a corner of the room during Come Hither to Me! In this robot theater the robotic agent charms the audience with her seductive humor and subtly enters them into a provocative dialogue that surfaces their stereotypical biases in gendered social interactions. Come Hither to Me! exemplifies “Interrogative Robot Theater,” our performative and critical method for social robotics research with an objective of designing robotic embodiment and interactivity for theatrical performances and public interventions. We apply various design and theater-making methods to develop a socially engaging, fun, and playful interactive experience for the audience. Using humorous conversation and embodied interaction design, our feminist robot theater makes a satirical performative commentary on misogynist dating culture and stereotypical gender roles. Inspired by the male-centered pickup artist community guidelines, we designed a chatbot decision tree for our female-gendered robot actor that flirts and provokes conversation with participants of all genders, subverting the imbalanced power dynamics of sexist social interactions. This interventionist theater-making methodology builds upon social justice-oriented interaction design, interrogative design, and Theater of the Oppressed. Through the application of this approach, Come Hither to Me! interrogates and problematizes gendered intimacy and agency in social interactions.
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Tahvanainen, Laura, Birgitta Tetri, and Outi Ahonen. "Exploring and Extending Human-Centered Design to Develop AI-Enabled Wellbeing Technology in Healthcare." In Communications in Computer and Information Science, 288–306. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59091-7_19.
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AbstractDigital transformation and digitalisation are rapidly affecting the society. The gradually increasing applications of different types of AI into solutions and services are welcome, but there are associated risks. These include, for example, within human aspects of care undermining fundamental rights, ethical considerations, sustainability, and policies and regulations. This change permeates every societal level, but it is especially evident in the healthcare sector due to the ageing population and shortage of professionals. This situation also places pressure on the development of competencies among healthcare professionals. A human-centered approach in design and design methods can promote the development of AI-based solutions in transdisciplinary and cross-disciplinary processes encompassing numerous stakeholders, scientific orientations, and perspectives. There is a need for research and evaluation of Human-Centered Design (HCD) processes and design methods to develop and gain more insights for future development.This study was conducted as research through design. It aimed to elucidate the application and insights gained from the adopted Service design process for AI-enabled services and HCD approach while developing AI-empowered solution, Voima-chatbot. One of this research's main conclusions and realization is the shift from purely HCD towards Life-Centered design of AI-enabled solutions with a human-in-the-loop. In addition, this project increased the understanding of the deep importance of having a transdisciplinary dialogue with developers during the process of developing digital well-being devices and combining different professional competencies to achieve the best working solutions.
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Zgraggen, Cyril R., Sebastian B. Kunz, and Kerstin Denecke. "Crowdsourcing for Creating a Dataset for Training a Medication Chatbot." In Studies in Health Technology and Informatics. IOS Press, 2021. http://dx.doi.org/10.3233/shti210364.
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To facilitate interaction with mobile health applications, chatbots are increasingly used. They realize the interaction as a dialog where users can ask questions and get answers from the chatbot. A big challenge is to create a comprehensive knowledge base comprising patterns and rules for representing possible user queries the chatbot has to understand and interpret. In this work, we assess how crowdsourcing can be used for generating examples of possible user queries for a medication chatbot. Within one week, the crowdworker generated 2‘738 user questions. The examples provide a large variety of possible formulations and information needs. As a next step, these examples for user queries will be used to train our medication chatbot.
Conference papers on the topic "Chatbot dialog"
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Petukhova, Kseniia, Veronika Smilga, and Dilyara Zharikova. "Abstract User Goals in Open-Domain Dialog Systems." In INTERNATIONAL CONFERENCE on Computational Linguistics and Intellectual Technologies. RSUH, 2023. http://dx.doi.org/10.28995/2075-7182-2023-22-1097-1107.
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In task-oriented dialog systems, conversational agents have the means to plan the dialog to accomplish user tasks (e.g., order pizza). In chit-chat systems, there are no such straightforward tasks. Yet, in chit-chat dialogs people still pursue goals, but these goals are more abstract and thus less formalizable. In this work, we describe the development process of two goal-aware prototypes of a chatbot. The first prototype features entirely human-crafted scenarios for seven topic-specific (low-level) goals and a Goal Tracker service that detects these goals and monitors the process of their achievement. The other one combines pre-written utterances with response generation using DialoGPT model to cover the scenarios of four general (high-level) goals. The results show that introducing the concept of goals improves performance of a chit-chat dialog system. Qualitative analysis of conversations with the High-Level goals prototype demonstrates cases where a goal-aware chatbot outperforms the original one.
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Adesina, Adewala. "iNOUN Chatbot: Providing Support and Microlearning with a Web Based Conversational Smart Assistant." In Tenth Pan-Commonwealth Forum on Open Learning. Commonwealth of Learning, 2022. http://dx.doi.org/10.56059/pcf10.8217.
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Innovative solutions based on Artificial Intelligence (AI) are transforming modern society. The goal of AI is to create computers or machines that imitate human abilities as closely as possible. Conversational interfaces, or chatbots, are considered a revolutionary step towards the next generation of digital experiences. Few studies, however, have examined how these tools can be used to increase access to education and content-driven support. This study describes and demonstrates an intelligent support personal bot that responds to simple questions on information on the university website. Furthermore, the chatbot can provide learners with small chunks of learning material via voice and text input. The iNOUN chatbot uses Drupal's content management system to serve content, Google's Dialog flow NLU tools to process text and voice input, and a chatbot interface to interact with users. The web-based system powered by Artificial Intelligence will offer personalized support to learners as well as serve as a teaching assistant capable of presenting small chunks of content to learners. As a result, equity, inclusion, and lifelong learning are likely to be promoted.
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Ilievski, Vladimir, Claudiu Musat, Andreea Hossman, and Michael Baeriswyl. "Goal-Oriented Chatbot Dialog Management Bootstrapping with Transfer Learning." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/572.
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Goal-Oriented (GO) Dialogue Systems, colloquially known as goal oriented chatbots, help users achieve a predefined goal (e.g. book a movie ticket) within a closed domain. A first step is to understand the user's goal by using natural language understanding techniques. Once the goal is known, the bot must manage a dialogue to achieve that goal, which is conducted with respect to a learnt policy. The success of the dialogue system depends on the quality of the policy, which is in turn reliant on the availability of high-quality training data for the policy learning method, for instance Deep Reinforcement Learning. Due to the domain specificity, the amount of available data is typically too low to allow the training of good dialogue policies. In this paper we introduce a transfer learning method to mitigate the effects of the low in-domain data availability. Our transfer learning based approach improves the bot's success rate by 20% in relative terms for distant domains and we more than double it for close domains, compared to the model without transfer learning. Moreover, the transfer learning chatbots learn the policy up to 5 to 10 times faster. Finally, as the transfer learning approach is complementary to additional processing such as warm-starting, we show that their joint application gives the best outcomes.
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Sangroya, Amit, C. Anantaram, Pratik Saini, and Mrinal Rawat. "Extracting Latent Beliefs and using Epistemic Reasoning to Tailor a Chatbot." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/860.
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During dialog with a customer for addressing his/her complaint the chatbot may pose questions or observations based on its underlying model. Sometimes the questions or observations posed may not be relevant given the nature of complaint and the current set of beliefs that the customer holds. In this paper we present a framework to build conversation system that addresses customer complaints in a meaningful manner using domain understanding, opinion analysis and epistemic reasoning. Extraction of latent beliefs assists in performing epistemic reasoning to maintain a meaningful conversation with the customer.
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Setiawan, Ariyono, Yuyun Suprapto, I. Gede Susrama Mas Diyasa, Chilyatun Nisa, Maulana Idris, Feronika Nur Maghfiro, Yuri Setiawan, and Dama Yanti Hilda. "Design and Development of Chatbot Using Dialog Flowin Surya Sembada PDAM Surabaya City." In International Joint Conference on Science and Engineering (IJCSE 2020). Paris, France: Atlantis Press, 2020. http://dx.doi.org/10.2991/aer.k.201124.021.
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Sudiatmika, I. Putu, and Made Ariantini. "Digital Marketing Chatbot Using API Dialog Flow Case Studi ITB Stikom Bali, Jimbaran Campus." In Proceedings of The 6th Asia-Pacific Education And Science Conference, AECon 2020, 19-20 December 2020, Purwokerto, Indonesia. EAI, 2021. http://dx.doi.org/10.4108/eai.19-12-2020.2309122.
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Raimer, Stephan, and Marleen Vanhauer. "Heuristic Evaluation of Public Service Chatbots." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001712.
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In recent years, chatbots have been adopted in business contexts and also for public services at a growing rate. Chatbots provide dialogue interfaces combining visual elements with natural conversation. Good Conversational Design in this context covers the topics of Natural-Language Processing (NLP) and Dialogue Management (DM). Few attention has been paid to the usability evaluation of conversational interfaces (Höhn & Bongard-Blanchy, 2021). The present paper builds upon the work by Höhn & Bongard-Blanchy by applying their framework of conversational heuristics to evaluate a set of public service chatbots operated in the federal state of Schleswig-Holstein. Thus, for each public service chatbot, a usability score is established and typical characteristics of public service chatbots in general are summarized. We discuss results by comparing the overall scores, weaknesses and strengths of each chatbot. In addition, we reflect on our experience in the application of the framework as well as highlight possible optimization potentials. Concludingly, this paper collects UX recommendations for public service chatbots.
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Cohn, Michelle, Chun-Yen Chen, and Zhou Yu. "A Large-Scale User Study of an Alexa Prize Chatbot: Effect of TTS Dynamism on Perceived Quality of Social Dialog." In Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/w19-5935.
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Paula, Robson T., Décio G. Aguiar Neto, Davi Romero, and Paulo T. Guerra. "Evaluation of Synthetic Datasets Generation for Intent Classification Tasks in Portuguese." In Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana. Sociedade Brasileira de Computação, 2021. http://dx.doi.org/10.5753/stil.2021.17806.
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A chatbot is an artificial intelligence based system aimed at chatting with users, commonly used as a virtual assistant to help people or answer questions. Intent classification is an essential task for chatbots where it aims to identify what the user wants in a certain dialogue. However, for many domains, little data are available to properly train those systems. In this work, we evaluate the performance of two methods to generate synthetic data for chatbots, one based on template questions and another based on neural text generation. We build four datasets that are used training chatbot components in the intent classification task. We intend to simulate the task of migrating a search-based portal to an interactive dialogue-based information service by using artificial datasets for initial model training. Our results show that template-based datasets are slightly superior to those neural-based generated in our application domain, however, neural-generated present good results and they are a viable option when one has limited access to domain experts to hand-code text templates.
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Ismail, Jabri, Aboulbichr Ahmed, and El ouaazizi Aziza. "Improving a Sequence-to-sequence NLP Model using a Reinforcement Learning Policy Algorithm." In 12th International Conference on Artificial Intelligence, Soft Computing and Applications. Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.122317.
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Nowadays, the current neural network models of dialogue generation(chatbots) show great promise for generating answers for chatty agents. But they are short-sighted in that they predict utterances one at a time while disregarding their impact on future outcomes. Modelling a dialogue’s future direction is critical for generating coherent, interesting dialogues, a need that has led traditional NLP dialogue models that rely on reinforcement learning. In this article, we explain how to combine these objectives by using deep reinforcement learning to predict future rewards in chatbot dialogue. The model simulates conversations between two virtual agents, with policy gradient methods used to reward sequences that exhibit three useful conversational characteristics: the flow of informality, coherence, and simplicity of response (related to forward-looking function). We assess our model based on its diversity, length, and complexity with regard to humans. In dialogue simulation, evaluations demonstrated that the proposed model generates more interactive responses and encourages a more sustained successful conversation. This work commemorates a preliminary step toward developing a neural conversational model based on the long-term success of dialogues.