Table of Contents

List of figures and tables

Introduction

PART I - HUMAN AND MACHINE APPROACHES TO TRANSLATION

1. Convergence of two approaches to translation

2. Levels of analysis

3. Predicative language models

PART II - MACHINE LEARNING TASKS IN TRANSLATION 4. Machine translation

5. Machine translation quality assessment and quality estimation

6. Intentionality and NLP tasks in translation

PART III - DATA IN HUMAN AND MACHINE LEARNING 7. Translation-computer interaction through language data

8. Balancing machine and human learning in translation

9. Impact of machine learning on translator education

Epilogue – Human-centered machine learning in translation

References

Index

About the Author

Peng Wang is a freelance conference interpreter with the Translation Bureau, Public Works and Government Services Canada, a part-time professor in the School of Translation and Interpretation, University of Ottawa and Course designer and instructor for Think NLP and Machine Translation Masterclass at the Localization Institute. She has published two books in Chinese, including Harry Potter and Its Chinese Translation.

David B. Sawyer is Director of Language Testing at the U.S. State Department’s Foreign Service Institute and a Senior Lecturer at the University of Maryland, USA. He is the author of Foundations of Interpreter Education: Curriculum and Assessment and co-editor of The Evolving Curriculum in Interpreter and Translator Education: Stakeholder Perspectives and Voices (both John Benjamins).

Reviews

"Machine Learning in Translation by Wang and Sawyer offers a new and important perspective on the topic by discussing machine learning concepts from a linguistic perspective. They offer an entryway to an in-depth understanding of machine learning concepts for linguists, closing a long-existing gap in literature suitable for machine learning education for this audience."Tabea De Wille, University of Limerick, Ireland