When NLP meets LLM by Munazza Zaib (.ePUB)+

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When NLP meets LLM: Neural Approaches to Context-based Conversational Question Answering by Munazza Zaib, Quan Z. Sheng, Wei Emma Zhang, Adnan Mahmood
Requirements: .ePUB, .PDF reader, 11.4 MB
Overview: This book looks at conversational search in intelligent dialogue systems, as it investigates and addresses the challenges pertinent to effective context incorporation in conversational question answering (ConvQA). The authors explore the possibility of designing a scalable Conversational Question Answering Agent that can handle the challenges of incomplete/ambiguous questions, better able to relate to co-references to cope with the problems of effective weights and optimal threshold selection in vehicular networks. A fundamental emphasis is the understanding of ambiguous follow-up questions and the generation of contextual and question entities to fill in the missing information gaps. Key topics are studied, such as ‘hard history selection’ to filter out the context that is not relevant and performing a re-ranking of the selected turns based on their significance to answer the question as a part of the soft history selection process. A quickly increasing number of research papers prove the promising potential and the growing interest of researchers from both academia and industry in conversational AI (ConvAI). ConvAI constitutes an integral part of Natural User Interfaces (NLIs) and is attracting attention from researchers in Information Retrieval (IR), Natural Language Processing (NLP), and Deep Learning (DL) communities.
Genre: Non-Fiction > Tech & Devices

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