Neural machine translation
Koehn, Philipp
Neural machine translation by Philipp Koehn - Cambridge : Cambridge University Press, ©2020 - xiv, 393 p. : ill. ; 26 cm.
Includes bibliographical references and index.
1. The Translation Problem 2. Uses of Machine Translation 3. History 4. Evaluation 5. Neural Networks 6. Computation Graphs 7. Neural Language Models 8. Neural Translation Models 9. Decoding 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
"A decade after the publication of my textbook Statistical Machine Translation, translation technology has changed drastically. As in other areas of artificial intelligence, deep neural networks have become the dominant paradigm, bringing with them impressive improvements of translation quality but also new challenges. What you are holding in your hands started out four years ago as a chapter of an envisioned second edition of that textbook, but the new way of doing things expanded so dramatically, and so little of previous methods is still relevant, that the text has grown into a book of its own right. Besides the chapter on evaluation, there is very little overlap between these two books. For the novice reader interested in machine translation, this is good news. We all started anew a few years ago, so you are not far behind in learning about the state of the art in the field"--
9781108497329
Machine translation.
Neural networks (Computer science)
Subject: Translating and interpreting -- Data processing.
418.020 / KOE-N
Neural machine translation by Philipp Koehn - Cambridge : Cambridge University Press, ©2020 - xiv, 393 p. : ill. ; 26 cm.
Includes bibliographical references and index.
1. The Translation Problem 2. Uses of Machine Translation 3. History 4. Evaluation 5. Neural Networks 6. Computation Graphs 7. Neural Language Models 8. Neural Translation Models 9. Decoding 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
"A decade after the publication of my textbook Statistical Machine Translation, translation technology has changed drastically. As in other areas of artificial intelligence, deep neural networks have become the dominant paradigm, bringing with them impressive improvements of translation quality but also new challenges. What you are holding in your hands started out four years ago as a chapter of an envisioned second edition of that textbook, but the new way of doing things expanded so dramatically, and so little of previous methods is still relevant, that the text has grown into a book of its own right. Besides the chapter on evaluation, there is very little overlap between these two books. For the novice reader interested in machine translation, this is good news. We all started anew a few years ago, so you are not far behind in learning about the state of the art in the field"--
9781108497329
Machine translation.
Neural networks (Computer science)
Subject: Translating and interpreting -- Data processing.
418.020 / KOE-N