Machine 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

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.

Part I: Foundation 1. Introduction to Machine Reading Comprehension 2. The Basics of Natural Language Processing 3. Deep Learning in Natural Language Processing

Part II: Architecture 4. Architecture of MRC Models 5. Common MRC Models 6. Pre-trained Language Model

Part III: Application 7. Code Analysis of SDNet Model 8. Applications and Future of Machine Reading Comprehension

Appendix A. Machine Learning Basics B. Deep Learning Basics