Neural Machine Translation

Neural Machine Translation
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Book Description
Deep learning is revolutionizing how machine translation systems are built today. This book introduces the challenge of machine translation and evaluation - including historical, linguistic, and applied context -- then develops the core deep learning methods used for natural language applications. Code examples in Python give readers a hands-on blueprint for understanding and implementing their own machine translation systems. The book also provides extensive coverage of machine learning tricks, issues involved in handling various forms of data, model enhancements, and current challenges and methods for analysis and visualization. Summaries of the current research in the field make this a state-of-the-art textbook for undergraduate and graduate classes, as well as an essential reference for researchers and developers interested in other applications of neural methods in the broader field of human language processing.

Content

Part I Introduction
1. The Translation Problem
2. Uses of Machine Translation
3. History
4. Evaluation

Part II Basics
5. Neural Networks
6. Computation Graphs
7. Neural Language Models
8. Neural Translation Models
9. Decoding

Part III Refinements
10. Machine Learning Tricks
11. Alternate Architectures
12. Revisiting Words
13. Adaptation
14. Beyond Parallel Corpora
15. Linguistic Structure
16. Current Challenges
17. Analysis and Visualization

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