ISBN: 9781032323800 - machine learning in translation (23 resultados)

Paperback. Condición: New. Brand new book, sourced directly from publisher. Dispatch time is 7-8 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Condición: New.

Condición: New. pp.218 1st NO-PA16APR2015-KAP.

Condición: New. pp.218.

Condición: As New. Unread book in perfect condition.

Condición: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Condición: New. pp.218.

Paperback. Condición: New. 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.Providing an exploration of the common ground between human and machine learning and of the nature of translation that leverages this new dimension, this book helps linguists, translators, and localizers better find their added value in a ML-driven translation environment. Part One explores how humans and machines approach the problem of translation in their own particular ways, in terms of word embeddings, chunking of larger meaning units, and prediction in translation based upon the broader context. Part Two introduces key tasks, including machine translation, translation quality assessment and quality estimation, and other Natural Language Processing (NLP) tasks in translation. Part Three focuses on the role of data in both human and machine learning processes. It proposes that a translator's unique value lies in the capability to create, manage, and leverage language data in different ML tasks in the translation process. It outlines new knowledge and skills that need to be incorporated into traditional translation education in the machine learning era. The book concludes with a discussion of human-centered machine learning in translation, stressing the need to empower translators with ML knowledge, through communication with ML users, developers, and programmers, and with opportunities for continuous learning.This accessible guide is designed for current and future users of ML technologies in localization workflows, including students on courses in translation and localization, language technology, and related areas. It supports the professional development of translation practitioners, so that they can fully utilize ML technologies and design their own human-centered ML-driven translation workflows and NLP tasks.…

Condición: New.

Condición: New.

Paperback / softback. Condición: New. New copy - Usually dispatched within 4 working days.

Condición: As New. Unread book in perfect condition.

Condición: New. 2023. 1st Edition. Paperback. . . . . .

Paperback. Condición: Brand New. 248 pages. 9.19x6.13x0.46 inches. In Stock.

Condición: New. 2023. 1st Edition. Paperback. . . . . . Books ship from the US and Ireland.

Taschenbuch. Condición: Neu. Machine Learning in Translation | Peng Wang (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2023 | Routledge | EAN 9781032323800 | Verantwortliche Person für die EU: Taylor and Francis, Taylor & Francis Verlag Gmbh, Kaufingerstr 24, 80331 München, gpsr[at]taylorandfrancis[dot]com | Anbieter: preigu. …

Paperback. Condición: New. 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.Providing an exploration of the common ground between human and machine learning and of the nature of translation that leverages this new dimension, this book helps linguists, translators, and localizers better find their added value in a ML-driven translation environment. Part One explores how humans and machines approach the problem of translation in their own particular ways, in terms of word embeddings, chunking of larger meaning units, and prediction in translation based upon the broader context. Part Two introduces key tasks, including machine translation, translation quality assessment and quality estimation, and other Natural Language Processing (NLP) tasks in translation. Part Three focuses on the role of data in both human and machine learning processes. It proposes that a translator's unique value lies in the capability to create, manage, and leverage language data in different ML tasks in the translation process. It outlines new knowledge and skills that need to be incorporated into traditional translation education in the machine learning era. The book concludes with a discussion of human-centered machine learning in translation, stressing the need to empower translators with ML knowledge, through communication with ML users, developers, and programmers, and with opportunities for continuous learning.This accessible guide is designed for current and future users of ML technologies in localization workflows, including students on courses in translation and localization, language technology, and related areas. It supports the professional development of translation practitioners, so that they can fully utilize ML technologies and design their own human-centered ML-driven translation workflows and NLP tasks.…

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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.Providing an exploration of the common ground between human and machine learning and of the nature of translation that leverages this new dimension, this book helps linguists, translators, and localizers better find their added value in a ML-driven translation environment. Part One explores how humans and machines approach the problem of translation in their own particular ways, in terms of word embeddings, chunking of larger meaning units, and prediction in translation based upon the broader context. Part Two introduces key tasks, including machine translation, translation quality assessment and quality estimation, and other Natural Language Processing (NLP) tasks in translation. Part Three focuses on the role of data in both human and machine learning processes. It proposes that a translator's unique value lies in the capability to create, manage, and leverage language data in different ML tasks in the translation process. It outlines new knowledge and skills that need to be incorporated into traditional translation education in the machine learning era. The book concludes with a discussion of human-centered machine learning in translation, stressing the need to empower translators with ML knowledge, through communication with ML users, developers, and programmers, and with opportunities for continuous learning.This accessible guide is designed for current and future users of ML technologies in localization workflows, including students on courses in translation and localization, language technology, and related areas. It supports the professional development of translation practitioners, so that they can fully utilize ML technologies and design their own human-centered ML-driven translation workflows and NLP tasks. 220 pp. Englisch.…

Paperback / softback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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.…

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.Providing an exploration of the common ground between human and machine learning and of the nature of translation that leverages this new dimension, this book helps linguists, translators, and localizers better find their added value in a ML-driven translation environment. Part One explores how humans and machines approach the problem of translation in their own particular ways, in terms of word embeddings, chunking of larger meaning units, and prediction in translation based upon the broader context. Part Two introduces key tasks, including machine translation, translation quality assessment and quality estimation, and other Natural Language Processing (NLP) tasks in translation. Part Three focuses on the role of data in both human and machine learning processes. It proposes that a translator's unique value lies in the capability to create, manage, and leverage language data in different ML tasks in the translation process. It outlines new knowledge and skills that need to be incorporated into traditional translation education in the machine learning era. The book concludes with a discussion of human-centered machine learning in translation, stressing the need to empower translators with ML knowledge, through communication with ML users, developers, and programmers, and with opportunities for continuous learning.This accessible guide is designed for current and future users of ML technologies in localization workflows, including students on courses in translation and localization, language technology, and related areas. It supports the professional development of translation practitioners, so that they can fully utilize ML technologies and design their own human-centered ML-driven translation workflows and NLP tasks.…