Cross-platform language learning: A spatial perspective on narratives of language learning across digital platforms

Lee, Yeong-Ju; Roger, Peter

doi:10.1016/j.system.2023.103145

Cited by 2 publications

(1 citation statement)

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“…To further accelerate chatbot training, cross-platform learning could emerge as a key factor [216]. By leveraging interaction data from different platforms and contexts, chatbots could broaden their understanding, improve their adaptability, and minimize errors across various conversational scenarios.…”

Section: Evolution Of Error Correction In Chatbotsmentioning

confidence: 99%

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: Evolution Of Error Correction In Chatbotsmentioning

confidence: 99%