Machine Reading Comprehension
Algorithms and Practice
- 1st Edition - March 20, 2021
- Imprint: Elsevier
- Author: Chenguang Zhu
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 0 1 1 8 - 5
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 0 1 1 9 - 2
Machine reading comprehension (MRC) is a cutting-edge technology in natural language processing (NLP). MRC has recently advanced significantly, surpassing human parity in se… Read more
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Request a sales quoteMachine reading comprehension (MRC) is a cutting-edge technology in natural language processing (NLP). MRC has recently advanced significantly, surpassing human parity in several public datasets. It has also been widely deployed by industry in search engine and quality assurance systems. Machine Reading Comprehension: Algorithms and Practice performs a deep-dive into MRC, offering a resource on the complex tasks this technology involves. The title presents the fundamentals of NLP and deep learning, before introducing the task, models, and applications of MRC. This volume gives theoretical treatment to solutions and gives detailed analysis of code, and considers applications in real-world industry. The book includes basic concepts, tasks, datasets, NLP tools, deep learning models and architecture, and insight from hands-on experience. In addition, the title presents the latest advances from the past two years of research. Structured into three sections and eight chapters, this book presents the basis of MRC; MRC models; and hands-on issues in application. This book offers a comprehensive solution for researchers in industry and academia who are looking to understand and deploy machine reading comprehension within natural language processing.
- Presents the first comprehensive resource on machine reading comprehension (MRC)
- Performs a deep-dive into MRC, from fundamentals to latest developments
- Offers the latest thinking and research in the field of MRC, including the BERT model
- Provides theoretical discussion, code analysis, and real-world applications of MRC
- Gives insight from research which has led to surpassing human parity in MRC
Researchers working on NLP, and particularly on MRC, in both industry and academia. Postgraduate and advanced students in machine learning, deep learning, NLP and aligned areas in computer science
- Cover image
- Title page
- Table of Contents
- Copyright
- About the author
- Foreword by Xuedong Huang
- Foreword by Zide Du
- Preface
- Acknowledgment
- Recommendation
- Part I: Foundation
- Chapter 1. Introduction to machine reading comprehension
- Abstract
- 1.1 The machine reading comprehension task
- 1.2 Natural language processing
- 1.3 Deep learning
- 1.4 Evaluation of machine reading comprehension
- 1.5 Machine reading comprehension datasets
- 1.6 How to make an machine reading comprehension dataset
- 1.7 Summary
- References
- Chapter 2. The basics of natural language processing
- Abstract
- 2.1 Tokenization
- 2.2 The cornerstone of natural language processing: word vectors
- 2.3 Linguistic tagging
- 2.4 Language model
- 2.5 Summary
- Reference
- Chapter 3. Deep learning in natural language processing
- Abstract
- 3.1 From word vector to text vector
- 3.2 Answer multiple-choice questions: natural language understanding
- 3.3 Write an article: natural language generation
- 3.4 Keep focused: attention mechanism
- 3.5 Summary
- Part II: Architecture
- Chapter 4. Architecture of machine reading comprehension models
- Abstract
- 4.1 General architecture of machine reading comprehension models
- 4.2 Encoding layer
- 4.3 Interaction layer
- 4.4 Output layer
- 4.5 Summary
- References
- Chapter 5. Common machine reading comprehension models
- Abstract
- 5.1 Bidirectional attention flow model
- 5.2 R-NET
- 5.3 FusionNet
- 5.4 Essential-term-aware retriever–reader
- 5.5 Summary
- References
- Chapter 6. Pretrained language models
- Abstract
- 6.1 Pretrained models and transfer learning
- 6.2 Translation-based pretrained language model: CoVe
- 6.3 Pretrained language model ELMo
- 6.4 The generative pretraining language model: generative pre-training (GPT)
- 6.5 The phenomenal pretrained language model: BERT
- 6.6 Summary
- References
- Part III: Application
- Chapter 7. Code analysis of the SDNet model
- Abstract
- 7.1 Multiturn conversational machine reading comprehension model: SDNet
- 7.2 Introduction to code
- 7.3 Preprocessing
- 7.4 Training
- 7.5 Batch generator
- 7.6 SDNet model
- 7.7 Summary
- Reference
- Chapter 8. Applications and future of machine reading comprehension
- Abstract
- 8.1 Intelligent customer service
- 8.2 Search engine
- 8.3 Health care
- 8.4 Laws
- 8.5 Finance
- 8.6 Education
- 8.7 The future of machine reading comprehension
- 8.8 Summary
- References
- Appendix A. Machine learning basics
- A.1 Types of machine learning
- A.2 Model and parameters
- A.3 Generalization and overfitting
- Appendix B. Deep learning basics
- B.1 Neural network
- B.2 Common types of neural network in deep learning
- B.3 The deep learning framework PyTorch
- Index
- Edition: 1
- Published: March 20, 2021
- No. of pages (Paperback): 270
- No. of pages (eBook): 270
- Imprint: Elsevier
- Language: English
- Paperback ISBN: 9780323901185
- eBook ISBN: 9780323901192
CZ
Chenguang Zhu
Principal Research Manager in Microsoft Corporation. Dr. Zhu obtained his Ph.D. in Computer Science from Stanford University. He is leading efforts in research and productization of natural language processing in Azure Cognitive AI. Dr. Zhu is proficient in artificial intelligence, deep learning and natural language processing, specializing in machine reading comprehension, text summarization and dialogue understanding. He has led teams to win the first place in the SQuAD 1.0 Machine Reading Comprehension Competition held by Stanford University, and reach human parity in the CoQA Conversational Reading Comprehension Competition. He has 40 papers published in top AI and NLP conferences such as ACL, EMNLP, NAACL and ICLR with more than 1,000 citations.
Affiliations and expertise
Principal Research Manager, Microsoft Corporation, USARead Machine Reading Comprehension on ScienceDirect