{"product_id":"when-nlp-meets-llm-neural-approaches-to-context-based-conversational-question-answering-hardcover","title":"When Nlp Meets LLM: Neural Approaches to Context-Based Conversational Question Answering - Hardcover","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eMunazza Zaib\u003c\/b\u003e (Author), \u003cb\u003eQuan Z. Sheng\u003c\/b\u003e (Author), \u003cb\u003eWei Emma Zhang\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis 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.\u003c\/p\u003e\u003cp\u003eThis book aims to demonstrate that the history selection and modelling approaches proposed can effectively improve the performance of ConvQA models in different settings. The proposed models are compared with the state-of-the-art vis-à-vis different conversational datasets and provide new insights into conversational information retrieval. Through a systematic study of structured representations, entity-aware history selection, and open-domain passage retrieval using contrastive learning, this book presents a robust framework for advancing multi-turn QA systems.\u003c\/p\u003e\u003cp\u003eIt is an essential resource for researchers, practitioners, and graduate students working at the intersection of NLP, dialogue systems, and intelligent information access.\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eMunazza Zaib\u003c\/b\u003e is currently a Postdoctoral Research Fellow at the Department of Human Centred Computing, Faculty of Information Technology, Monash University, Australia.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eQuan Z. Sheng\u003c\/b\u003e is a Distinguished Professor and Head of School of Computing at Macquarie University, Australia. ). He is the recipient of the AMiner Most Influential Scholar Award on IoT (2007-2017), ARC (Australian Research Council) Future Fellowship (2014).\u003c\/p\u003e\u003cp\u003e\u003cb\u003eWei Emma Zhang\u003c\/b\u003e is Associate Head of People and Culture at the School of Computer and Mathematical Sciences, and a researcher at the Australian Institute for Machine Learning, the University of Adelaide.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eAdnan Mahmood\u003c\/b\u003e is a Lecturer in Computing - IoT and Networking at the School of Computing, Macquarie University, Sydney.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 102\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.5 x 8.5 x 5.44 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e October 16, 2025\u003c\/div\u003e\n            ","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":47509483618482,"sku":"9781032970844","price":118.24,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0770\/3891\/1666\/files\/vFA4A2BpBx9781032970844.webp?v=1779627346","url":"https:\/\/box.dadyminds.org\/products\/when-nlp-meets-llm-neural-approaches-to-context-based-conversational-question-answering-hardcover","provider":"DADYMINDS BOX","version":"1.0","type":"link"}