Machine Learning in Translation introduces machine learning (ML) theories and technologies that are most relevant to translation processes, approaching the topic from a human perspective and emphasizing that ML and ML-driven technologies are tools for humans.
Machine Learning in Translation introduces machine learning (ML) theories and technologies that are most relevant to translation processes, approaching the topic from a human perspective and emphasizing that ML and ML-driven technologies are tools for humans.
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).
Inhaltsangabe
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
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
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