Full Description
Translation technology is essential for translation students, practising translators, and those working as part of the language services industry, but looming above others are the tools for automating translation: machine translation and, more recently, generative AI based on large language models (LLMs).
This book, authored by leading experts, demystifies machine translation, explaining its origins, its training data, how neural machine translation and LLMs work, how to measure their quality, how translators interact with contemporary systems for automating translation, and how readers can build their own machine translation or LLM. In later chapters, the scope of the book expands to look more broadly at translation automation in audiovisual translation and localisation. Importantly, the book also examines the sociotechnical context, focusing on ethics and sustainability. Enhanced with activities, further reading and resource links, including online support material on the Routledge Translation studies portal, this is an essential textbook for students of translation studies, trainee and practising translators, and users of MT and multilingual LLMs.
Additional resources are available on the Routledge Translation Studies Portal.
Contents
Contents
Series Editor's Foreword
Preface
Abbreviations and Acronyms
Chapter 1 - The Roots of Machine Translation
Chapter 2 - Data for Machine Translation
Chapter 3 - Translation Memory and Computer-Assisted Translation tools
Chapter 4 - Neural Networks and Neural Machine Translation
Chapter 5 - Machine Translation Evaluation
Chapter 6 - Neural Machine Translation: Build or Buy?
Chapter 7 - Building Machine Translation Models with Colab
Chapter 8 - Machine Translation Post-Editing
Chapter 9 - Machine Translation in Multimedia Translation and Localisation
Chapter 10 - Large Language Models and Multilingual Language Models: The Future of Machine Translation?
Chapter 11 - Sociotechnical Effects of Machine Translation
Afterword
Glossary
