When NLP meets LLM: Neural Approaches to Context-based Conversational Question Answering
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.

This 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.

It is an essential resource for researchers, practitioners, and graduate students working at the intersection of NLP, dialogue systems, and intelligent information access.

1147832358
When NLP meets LLM: Neural Approaches to Context-based Conversational Question Answering
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.

This 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.

It is an essential resource for researchers, practitioners, and graduate students working at the intersection of NLP, dialogue systems, and intelligent information access.

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When NLP meets LLM: Neural Approaches to Context-based Conversational Question Answering

When NLP meets LLM: Neural Approaches to Context-based Conversational Question Answering

When NLP meets LLM: Neural Approaches to Context-based Conversational Question Answering
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When NLP meets LLM: Neural Approaches to Context-based Conversational Question Answering

When NLP meets LLM: Neural Approaches to Context-based Conversational Question Answering

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.

This 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.

It is an essential resource for researchers, practitioners, and graduate students working at the intersection of NLP, dialogue systems, and intelligent information access.

Product Details

ISBN-13: 9781032970844
Publisher: CRC Press
Publication date: 10/16/2025
Pages: 102
Product dimensions: 5.44(w) x 8.50(h) x (d)

About the Author

Munazza Zaib is currently a Postdoctoral Research Fellow at the Department of Human Centred Computing, Faculty of Information Technology, Monash University, Australia.

Quan Z. Sheng 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).

Wei Emma Zhang 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.

Adnan Mahmood is a Lecturer in Computing – IoT and Networking at the School of Computing, Macquarie University, Sydney.

Table of Contents

1. Introduction 2. Role of Conversational Question Answering in Artificial Intelligence 3. Resolving Conversational Dependencies in Conversational Question Answering 4. Dynamic History Selection for Conversational Question Answering 5. History Modeling for Open-Domain Conversational Question Answering 6. Conclusion and Future Directions

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