Peng Wang, David B. Sawyer

Machine Learning in Translation

• Gebundenes Buch

Produktbeschreibung

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.

---

Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.

Produktdetails

• Verlag: Taylor & Francis Ltd
• Seitenzahl: 206
• Erscheinungstermin: 12. April 2023
• Englisch
• Abmessung: 234mm x 156mm x 14mm
• Gewicht: 446g
• ISBN-13: 9781032343228
• ISBN-10: 1032343222
• Artikelnr.: 66746472

Autorenporträt

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

Rezensionen

"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