Chatbot Components and Architectures

Galitsky, Boris

doi:10.1007/978-3-030-04299-8_2

Cited by 11 publications

(7 citation statements)

References 43 publications

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“…At its core, data-driven learning involves the systematic analysis of vast amounts of data to enable chatbots to improve their conversational abilities over time. By leveraging diverse and comprehensive datasets, chatbots can better understand user intents, preferences, and behaviors, allowing them to generate more accurate and contextually relevant responses [77].…”

Section: Learning Algorithms Specific To Chatbotsmentioning

confidence: 99%

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Error Correction and Adaptation in Conversational AI: A Review of Techniques and Applications in Chatbots

Izadi,

Forouzanfar

2024

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This study explores the progress of chatbot technology, focusing on the aspect of error correction to enhance these smart conversational tools. Chatbots, powered by artificial intelligence (AI), are increasingly prevalent across industries such as customer service, healthcare, e-commerce, and education. Despite their use and increasing complexity, chatbots are prone to errors like misunderstandings, inappropriate responses, and factual inaccuracies. These issues can have an impact on user satisfaction and trust. This research provides an overview of chatbots, conducts an analysis of errors they encounter, and examines different approaches to rectifying these errors. These approaches include using data-driven feedback loops, involving humans in the learning process, and adjusting through learning methods like reinforcement learning, supervised learning, unsupervised learning, semi-supervised learning, and meta-learning. Through real life examples and case studies in different fields, we explore how these strategies are implemented. Looking ahead, we explore the different challenges faced by AI-powered chatbots, including ethical considerations and biases during implementation. Furthermore, we explore the transformative potential of new technological advancements, such as explainable AI models, autonomous content generation algorithms (e.g., generative adversarial networks), and quantum computing to enhance chatbot training. Our research provides information for developers and researchers looking to improve chatbot capabilities, which can be applied in service and support industries to effectively address user requirements.

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Section: Learning Algorithms Specific To Chatbotsmentioning

confidence: 99%

“…Continuity and context preservation: Preserving context throughout a conversation, even after corrections are made, is key to natural dialogue flow. Chatbots should be capable of referencing previous parts of the conversation to maintain continuity [77].…”

Section: Balancing Error Correction With Maintaining Conversational Flowmentioning

confidence: 99%

Error Correction and Adaptation in Conversational AI: A Review of Techniques and Applications in Chatbots

Izadi,

Forouzanfar

2024

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“…However, various technologies are at work behind the scenes to ensure smooth interaction. Natural Language Understanding (NLU unit) and Natural language Generation (NLG unit) are the major components of conversational agent architecture [12]. Figure 2 illustrates the architecture of conversational agents.…”

Section: Background -Working Of Conversational Agentsmentioning

confidence: 99%

AI-Based Conversational Agents: A Scoping Review From Technologies to Future Directions

et al. 2022

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Artificial intelligence is changing the world, especially the interaction between machines and humans. Learning and interpreting natural languages and responding have paved the way for many technologies and applications. The amalgam of machine learning, deep learning, and natural language processing helped Conversational Artificial Intelligence (AI) to change the face of Human-Computer Interaction (HCI). A conversational agent is an excellent example of conversational AI, which imitates the natural language. This article presents a sweeping overview of conversational agents that includes different techniques such as pattern-based, machine learning, and deep learning used to implement conversational agents. It also discusses the panorama of different tasks in conversational agents. This study also focuses on how conversational agents can simulate human behavior by adding emotions, sentiments, and affect to the context. With the advancements in recent trends and the rise in deep learning models, the authors review the deep learning techniques and various publicly available datasets used in conversational agents. This article unearths the research gaps in conversational agents and gives insights into future directions.

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“…Passing is the main task of natural language understandings (NLU) which takes the string of words and generates the linguistic structure of the statement. It uses the context free grammars pattern matching for data driven techniques [ 31 ]. NLG: NLG uses templates and text stored in knowledge base to generate the response.…”

Section: Ai-chatbot: Architectural Componentsmentioning

confidence: 99%

A survey on providing customer and public administration based services using AI: chatbot

Nirala

Singh

Purani³

2022

Multimed Tools Appl

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A chatbot is emerged as an effective tool to address the user queries in automated, most appropriate and accurate way. Depending upon the complexity of the subject domain, researchers are employing variety of soft-computing techniques to make the chatbot user-friendly. It is observed that chatbots have flooded the globe with wide range of services including ordering foods, suggesting products, advising for insurance policies, providing customer support, giving financial assistance, schedule meetings etc. However, public administration based services wherein chatbot intervention influence the most, is not explored yet. This paper discuses about artificial intelligence based chatbots including their applications, challenges, architecture and models. It also talks about evolution of chatbots starting from Turing Test and Rule-based chatbots to advanced Artificial Intelligence based Chatbots (AI-Chatbots). AI-Chatbots are providing much kind of services, which this paper outlines into two main aspects including customer based services and public administration based services. The purpose of this survey is to understand and explore the possibility of customer & public administration services based chatbot. The survey demonstrates that there exist an immense potential in the AI assisted chatbot system for providing customer services and providing better governance in public administration services.

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Chatbot Components and Architectures

Cited by 11 publications

References 43 publications

Error Correction and Adaptation in Conversational AI: A Review of Techniques and Applications in Chatbots

Error Correction and Adaptation in Conversational AI: A Review of Techniques and Applications in Chatbots

AI-Based Conversational Agents: A Scoping Review From Technologies to Future Directions

A survey on providing customer and public administration based services using AI: chatbot

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