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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.
List of 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
About the author
Joss Moorkens is Associate Professor at the School of Applied Language and Intercultural Studies and Science Lead at the ADAPT Centre at Dublin City University, Ireland. He is General Co- Editor of the journal Translation Spaces, author and editor of several books, articles, chapters, and special issues on translation technology, and sits on the board of the European Masters in Translation network.
Andy Way is Professor of Computing and Co- Founder of the ADAPT Centre at Dublin City University, Ireland. He was previously editor of the Machine Translation journal for 15 years, and president of both the European and International Associations for Machine Translation. He has over 450 publications, including five books on machine translation.
Séamus Lankford is a Computer Science lecturer with over 25 years’ experience at the Munster Technological University, Ireland. He has published extensively on the topic of machine translation. The focus of his doctoral thesis was the enhancement of NMT of low- resource languages through corpus development, human evaluation, and explainable AI architectures.
Summary
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.
