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- Foreword
- Preface
- Contents
- Contributors
- Acronyms
- 1 A Joint Introduction to Natural Language Processing and to Deep Learning
- 2 Deep Learning in Conversational Language Understanding
- 3 Deep Learning in Spoken and Text-Based Dialog Systems
- 3.1 Introduction
- 3.2 Learning Methodology for Components of a Dialog System
- 3.3 Goal-Oriented Neural Dialog Systems
- 3.4 Model-Based User Simulators
- 3.5 Natural Language Generation
- 3.6 End-to-End Deep Learning Approaches to Building Dialog Systems
- 3.7 Deep Learning for Open Dialog Systems
- 3.8 Datasets for Dialog Modeling
- 3.9 Open Source Dialog Software
- 3.10 Dialog System Evaluation
- 3.11 Summary
- References
- 4 Deep Learning in Lexical Analysis and Parsing
- 5 Deep Learning in Knowledge Graph
- 6 Deep Learning in Machine Translation
- 6.1 Introduction
- 6.2 Statistical Machine Translation and Its Challenges
- 6.3 Component-Wise Deep Learning for Machine Translation
- 6.4 End-to-End Deep Learning for Machine Translation
- 6.4.1 The Encoder–Decoder Framework
- 6.4.2 Neural Attention in Machine Translation
- 6.4.3 Addressing Technical Challenges of Large Vocabulary
- 6.4.4 End-to-End Training to Optimize Evaluation Metric Directly
- 6.4.5 Incorporating Prior Knowledge
- 6.4.6 Low-Resource Language Translation
- 6.4.7 Network Structures in Neural Machine Translation
- 6.4.8 Combination of SMT and NMT
- 6.5 Summary
- References
- 7 Deep Learning in Question Answering
- 8 Deep Learning in Sentiment Analysis
- 9 Deep Learning in Social Computing
- 10 Deep Learning in Natural Language Generation from Images
- 10.1 Introduction
- 10.2 Background
- 10.3 Deep Learning Frameworks to Generate Natural Language from an Image
- 10.4 Evaluation Metrics and Benchmarks
- 10.5 Industrial Deployment of Image Captioning
- 10.6 Examples: Natural Language Descriptions of Images
- 10.7 Recent Research on Generating Stylistic Natural Language from Images
- 10.8 Summary
- References
- 11 Epilogue: Frontiers of NLP in the Deep Learning Era
- Appendix Glossary
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