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).

"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

• List of figures and tablesIntroductionPART I - HUMAN AND MACHINE APPROACHES TO TRANSLATION1. Convergence of two approaches to translation2. Levels of analysis3. Predicative language modelsPART II - MACHINE LEARNING TASKS IN TRANSLATION 4. Machine translation 5. Machine translation quality assessment and quality estimation6. Intentionality and NLP tasks in translationPART III - DATA IN HUMAN AND MACHINE LEARNING 7. Translation-computer interaction through language data8. Balancing machine and human learning in translation 9. Impact of machine learning on translator education Epilogue – Human-centered machine learning in translationReferencesIndex

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).

"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

• List of figures and tablesIntroductionPART I - HUMAN AND MACHINE APPROACHES TO TRANSLATION1. Convergence of two approaches to translation2. Levels of analysis3. Predicative language modelsPART II - MACHINE LEARNING TASKS IN TRANSLATION 4. Machine translation 5. Machine translation quality assessment and quality estimation6. Intentionality and NLP tasks in translationPART III - DATA IN HUMAN AND MACHINE LEARNING 7. Translation-computer interaction through language data8. Balancing machine and human learning in translation 9. Impact of machine learning on translator education Epilogue – Human-centered machine learning in translationReferencesIndex

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).

"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

• List of figures and tablesIntroductionPART I - HUMAN AND MACHINE APPROACHES TO TRANSLATION1. Convergence of two approaches to translation2. Levels of analysis3. Predicative language modelsPART II - MACHINE LEARNING TASKS IN TRANSLATION 4. Machine translation 5. Machine translation quality assessment and quality estimation6. Intentionality and NLP tasks in translationPART III - DATA IN HUMAN AND MACHINE LEARNING 7. Translation-computer interaction through language data8. Balancing machine and human learning in translation 9. Impact of machine learning on translator education Epilogue – Human-centered machine learning in translationReferencesIndex