Deep learning is revolutionizing how machine translation systems are built today. This introduction to machine translation starts from the basics of neural network methods and reaches the state of the art, while giving illuminating historical, linguistic, and applied context. Code examples in Python give a hands-on blueprint for implementation.
Inhaltsverzeichnis
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. Adaptations; 14. Beyond Parallel Corpora; 15. Linguistic Structure; 16. Current Challenges; 17. Analysis and Visualization.
