Neural Machine Translation

di Philipp Koehn (Autore)

Cambridge University Press, 2020

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.

• Autore: Philipp Koehn
• Editore: Cambridge University Press
• Anno: 2020
• Rilegatura: Hardback
• Pagine: 406 p.
• Dimensioni: 252 x 178 mm
• Peso: 840 gr.
• EAN: 9781108497329

Testo in English