Software-Based Dialogue Systems: Survey, Taxonomy, and Challenges

Motger, Quim; Franch, Xavier; Marco, Jordi

doi:10.1145/3527450

Cited by 36 publications

(5 citation statements)

References 158 publications

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“…As evidenced by further studies [6], the development of chatbots stimulated their use to solve problems in various areas of human activity: e-commerce, business support, health care, tourism, restaurant business and food, games, sports, etc. The study describes the possibilities of using deep learning technology for chatbots through the use of recurrent neural networks.…”

Section: The Study Materials and Methodsmentioning

confidence: 99%

Development of a natural language chatbot interface for website users

Lynnyk,

Krestyanpol,

Rozvod

2024

EEJET

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Today, there are a variety of technology solutions for creating chatbots, including universal platforms such as Google's Dialogflow, Microsoft's Azure Bot Service, and IBM's Watson Assistant, as well as chatbot builders such as ActiveChat, SendPulse, and BotPress. The disadvantage of the considered platforms and designers is the limitation of the free plan. This study considered the development and integration of an informative chatbot into the department's website in order to create a virtual assistant for potential applicants and applicants. The relevance of the study is due to the modern conditions of admission, limited opportunities to conduct career guidance events in connection with the consequences of the COVID 19 pandemic and the war on the territory of Ukraine. The object of research is a system of informing website users using natural language. A software product has been developed, which is an information system for applicants. The dynamic JavaScript programming language combined with the Node.js code execution environment and the Telegraf library was chosen to create the chatbot. The Heroku cloud platform with an integrated Heroku PostgreSQL database became the server for the operation of the chatbot. The system is implemented using the @BotFather Telegram service, which makes it possible to integrate it into any web resource. The stages of chatbot development are highlighted with examples of information, linguistic, and technical support. An analysis of the compliance of the developed chatbot with regard to the criteria of informativeness, multimodality, emotionality, productivity, and interactivity was carried out. The structure of the information content of the chatbot, which includes a set of modules, has been developed. A test version of the chatbot has been designed, tested, and integrated into the website of the Department of Applied Linguistics, which will continue to be tested and improved in 2023–2024. Practical significance: the structure of information content and the method of developing a chatbot could be used to develop authentic systems for informing applicants in institutions of higher education

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Section: The Study Materials and Methodsmentioning

confidence: 99%

Development of a natural language chatbot interface for website users

Lynnyk,

Krestyanpol,

Rozvod

2024

EEJET

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“…While the AI-enabled mobile chatbot psychologist offers a convenient and accessible platform for psychological support, there is a risk that users may become overly reliant on the chatbot, neglecting the importance of face-to-face interactions and professional psychological care [49]. It is crucial to emphasize that the chatbot is intended to complement, rather than replace, traditional mental health services and should be used as an adjunct to professional care as needed.…”

Section: E Ongoing Support and Maintenancementioning

confidence: 99%

Artificial Intelligence Enabled Mobile Chatbot Psychologist using AIML and Cognitive Behavioral Therapy

Omarov¹,

Zhumanov²,

Gumar³

et al. 2023

IJACSA

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In recent years, the demand for mental health services has increased exponentially, prompting the need for accessible, cost-effective, and efficient solutions. This paper introduces an Artificial Intelligence (AI) enabled mobile chatbot psychologist that leverages AIML (Artificial Intelligence Markup Language) and Cognitive Behavioral Therapy (CBT) to provide psychological support. The chatbot is designed to facilitate mental health care by offering personalized CBT interventions to individuals experiencing psychological distress. The proposed mobile chatbot psychologist employs AIML, a language created to facilitate human-computer interactions, to understand user inputs and generate contextually appropriate responses. To ensure the efficacy of the chatbot, it is equipped with a knowledge base comprising CBT principles and techniques, enabling it to provide targeted psychological interventions. The integration of CBT allows the chatbot to address a wide range of mental health issues, including anxiety, depression, stress, and phobias, by helping users identify and challenge cognitive distortions. The paper discusses the development and implementation of the mobile chatbot psychologist, detailing the AIML-based conversational engine and the incorporation of CBT techniques. The chatbot's effectiveness is evaluated through a series of user studies involving participants with varying levels of psychological distress. Results demonstrate the chatbot's ability to deliver personalized interventions, with users reporting significant improvements in their mental well-being. The AIenabled mobile chatbot psychologist offers a promising solution to bridge the gap in mental health care, providing an easily accessible, cost-effective, and scalable platform for psychological support. This innovative approach can serve as a valuable adjunct to traditional therapy and help reduce the burden on mental health professionals, while empowering individuals to take charge of their mental well-being.

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“…These technologies, which are characterized by their ability to deliver immediate and personalized responses, have greatly enhanced user interactions. The development of such systems typically falls into two distinct categories: rule-based systems and those that harness the power of artificial intelligence, particularly through machine learning algorithms [1].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Generic and Fine-Tuned Large Language Models for Conversational Agent Systems

Villa,

Carneros-Prado,

Dobrescu

et al. 2024

Robotics

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In the rapidly evolving domain of conversational agents, the integration of Large Language Models (LLMs) into Chatbot Development Platforms (CDPs) is a significant innovation. This study compares the efficacy of employing generic and fine-tuned GPT-3.5-turbo models for designing dialog flows, focusing on the intent and entity recognition crucial for dynamic conversational interactions. Two distinct approaches are introduced: a generic GPT-based system (G-GPT) leveraging the pre-trained model with complex prompts for intent and entity detection, and a fine-tuned GPT-based system (FT-GPT) employing customized models for enhanced specificity and efficiency. The evaluation encompassed the systems’ ability to accurately classify intents and recognize named entities, contrasting their adaptability, operational efficiency, and customization capabilities. The results revealed that, while the G-GPT system offers ease of deployment and versatility across various contexts, the FT-GPT system demonstrates superior precision, efficiency, and customization, although it requires initial training and dataset preparation. This research highlights the versatility of LLMs in enriching conversational features for talking assistants, from social robots to interactive chatbots. By tailoring these advanced models, the fluidity and responsiveness of conversational agents can be enhanced, making them more adaptable and effective in a variety of settings, from customer service to interactive learning environments.

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Software-Based Dialogue Systems: Survey, Taxonomy, and Challenges

Cited by 36 publications

References 158 publications

Development of a natural language chatbot interface for website users

Development of a natural language chatbot interface for website users

Artificial Intelligence Enabled Mobile Chatbot Psychologist using AIML and Cognitive Behavioral Therapy

Comparative Analysis of Generic and Fine-Tuned Large Language Models for Conversational Agent Systems

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