Neural Machine Translation

by Philipp Koehn

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.

Genres: Computer Science

406 pages, Kindle Edition

Published June 18, 2020

Reviews

[Akhror Kutbidinov · Rating 4 out of 5]

Again, a must read if you are in a field of AI or Machine Translation, connects to current LLMS, remember Machine Translation is the reason we got LLMS, in the journey of creating a better MT they accidentally created AI, ironic isn't it? (ironic partly because it is (MT) about to be replaced by LLM)