Machine Reading Comprehension: Algorithms and Practice

by Chenguang Zhu

Overview

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.

Product Details

ISBN-13:	9780323901185
Publisher:	Elsevier Science
Publication date:	03/26/2021
Pages:	270
Product dimensions:	6.00(w) x 9.00(h) x (d)

About the Author

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.

Table of Contents

Part I: Foundation1. Introduction to Machine Reading Comprehension2. The Basics of Natural Language Processing3. Deep Learning in Natural Language Processing

Part II: Architecture4. Architecture of MRC Models5. Common MRC Models6. Pre-trained Language Model

Part III: Application7. Code Analysis of SDNet Model8. Applications and Future of Machine Reading Comprehension

AppendixA. Machine Learning BasicsB. Deep Learning Basics

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From the Publisher

Presents, for the first time, detailed treatment of machine reading comprehension (MRC), a key technology within natural language processing (NLP), giving basics, models, and hands-on issues in application