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Online Public Access Catalogue

题名/责任者:

Machine learning in translation / [edited by] Peng Wang and David B. Sawyer.

出版发行项:

Abingdon, Oxon ; New York, NY : Routledge, Taylor & Francis Group, 2023.

ISBN:

9781032343228

ISBN:

1032343222

ISBN:

9781032323800

ISBN:

1032323809

载体形态项:

x, 206 pages : illustrations, forms ; 25 cm

附加个人名称:

Wang, Peng, 1975- editor.

附加个人名称:

Sawyer, David B., editor.

论题主题:

Translating and interpreting-Technological innovations.

论题主题:

Translating and interpreting-Data processing.

论题主题:

Machine learning.

中图法分类号:

H059-39

书目附注:

Includes bibliographical references (pages 189-202) and index.

摘要附注:

"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"--

豆瓣简介：