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Machine Reading Comprehension


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Table of Contents

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

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.

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