Machine learning in translation:
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 gr...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
London ; New York
Routledge, Taylor & Francis Group
2023
|
| Schlagworte: | |
| Zusammenfassung: | 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 |
| Beschreibung: | x, 206 Seiten Illustrationen |
| ISBN: | 9781032343228 9781032323800 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV048924908 | ||
| 003 | DE-604 | ||
| 005 | 20230613 | ||
| 007 | t | ||
| 008 | 230502s2023 a||| |||| 00||| eng d | ||
| 020 | |a 9781032343228 |c hardback |9 978-1-032-34322-8 | ||
| 020 | |a 9781032323800 |c paperback |9 978-1-032-32380-0 | ||
| 035 | |a (OCoLC)1374799490 | ||
| 035 | |a (DE-599)BVBBV048924908 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-29 | ||
| 100 | 1 | |a Wang, Peng |d 1975- |e Verfasser |0 (DE-588)128951089X |4 aut | |
| 245 | 1 | 0 | |a Machine learning in translation |c Peng Wang and David B. Sawyer |
| 264 | 1 | |a London ; New York |b Routledge, Taylor & Francis Group |c 2023 | |
| 300 | |a x, 206 Seiten |b Illustrationen | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 520 | |a 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. | ||
| 520 | |a 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. | ||
| 520 | |a 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 | ||
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Übersetzung |0 (DE-588)4061418-9 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Technischer Fortschritt |0 (DE-588)4059252-2 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Datenverarbeitung |0 (DE-588)4011152-0 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Übersetzung |0 (DE-588)4061418-9 |D s |
| 689 | 0 | 1 | |a Technischer Fortschritt |0 (DE-588)4059252-2 |D s |
| 689 | 0 | 2 | |a Datenverarbeitung |0 (DE-588)4011152-0 |D s |
| 689 | 0 | 3 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Sawyer, David B. |e Verfasser |0 (DE-588)1193212219 |4 aut | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-1-003-32153-8 |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-034188963 | ||
Datensatz im Suchindex
| _version_ | 1804185109806448640 |
|---|---|
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author | Wang, Peng 1975- Sawyer, David B. |
| author_GND | (DE-588)128951089X (DE-588)1193212219 |
| author_facet | Wang, Peng 1975- Sawyer, David B. |
| author_role | aut aut |
| author_sort | Wang, Peng 1975- |
| author_variant | p w pw d b s db dbs |
| building | Verbundindex |
| bvnumber | BV048924908 |
| ctrlnum | (OCoLC)1374799490 (DE-599)BVBBV048924908 |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>03568nam a2200433 c 4500</leader><controlfield tag="001">BV048924908</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20230613 </controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">230502s2023 a||| |||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781032343228</subfield><subfield code="c">hardback</subfield><subfield code="9">978-1-032-34322-8</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781032323800</subfield><subfield code="c">paperback</subfield><subfield code="9">978-1-032-32380-0</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1374799490</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV048924908</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-29</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wang, Peng</subfield><subfield code="d">1975-</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)128951089X</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Machine learning in translation</subfield><subfield code="c">Peng Wang and David B. Sawyer</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">London ; New York</subfield><subfield code="b">Routledge, Taylor & Francis Group</subfield><subfield code="c">2023</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">x, 206 Seiten</subfield><subfield code="b">Illustrationen</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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. </subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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. </subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Übersetzung</subfield><subfield code="0">(DE-588)4061418-9</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Technischer Fortschritt</subfield><subfield code="0">(DE-588)4059252-2</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Datenverarbeitung</subfield><subfield code="0">(DE-588)4011152-0</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Übersetzung</subfield><subfield code="0">(DE-588)4061418-9</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Technischer Fortschritt</subfield><subfield code="0">(DE-588)4059252-2</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Datenverarbeitung</subfield><subfield code="0">(DE-588)4011152-0</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="3"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sawyer, David B.</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1193212219</subfield><subfield code="4">aut</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Online-Ausgabe</subfield><subfield code="z">978-1-003-32153-8</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-034188963</subfield></datafield></record></collection> |
| id | DE-604.BV048924908 |
| illustrated | Illustrated |
| index_date | 2024-07-03T21:55:43Z |
| indexdate | 2024-07-10T09:50:01Z |
| institution | BVB |
| isbn | 9781032343228 9781032323800 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034188963 |
| oclc_num | 1374799490 |
| open_access_boolean | |
| owner | DE-29 |
| owner_facet | DE-29 |
| physical | x, 206 Seiten Illustrationen |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Routledge, Taylor & Francis Group |
| record_format | marc |
| spelling | Wang, Peng 1975- Verfasser (DE-588)128951089X aut Machine learning in translation Peng Wang and David B. Sawyer London ; New York Routledge, Taylor & Francis Group 2023 x, 206 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier 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 Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Übersetzung (DE-588)4061418-9 gnd rswk-swf Technischer Fortschritt (DE-588)4059252-2 gnd rswk-swf Datenverarbeitung (DE-588)4011152-0 gnd rswk-swf Übersetzung (DE-588)4061418-9 s Technischer Fortschritt (DE-588)4059252-2 s Datenverarbeitung (DE-588)4011152-0 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 Sawyer, David B. Verfasser (DE-588)1193212219 aut Erscheint auch als Online-Ausgabe 978-1-003-32153-8 |
| spellingShingle | Wang, Peng 1975- Sawyer, David B. Machine learning in translation Maschinelles Lernen (DE-588)4193754-5 gnd Übersetzung (DE-588)4061418-9 gnd Technischer Fortschritt (DE-588)4059252-2 gnd Datenverarbeitung (DE-588)4011152-0 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)4061418-9 (DE-588)4059252-2 (DE-588)4011152-0 |
| title | Machine learning in translation |
| title_auth | Machine learning in translation |
| title_exact_search | Machine learning in translation |
| title_exact_search_txtP | Machine learning in translation |
| title_full | Machine learning in translation Peng Wang and David B. Sawyer |
| title_fullStr | Machine learning in translation Peng Wang and David B. Sawyer |
| title_full_unstemmed | Machine learning in translation Peng Wang and David B. Sawyer |
| title_short | Machine learning in translation |
| title_sort | machine learning in translation |
| topic | Maschinelles Lernen (DE-588)4193754-5 gnd Übersetzung (DE-588)4061418-9 gnd Technischer Fortschritt (DE-588)4059252-2 gnd Datenverarbeitung (DE-588)4011152-0 gnd |
| topic_facet | Maschinelles Lernen Übersetzung Technischer Fortschritt Datenverarbeitung |
| work_keys_str_mv | AT wangpeng machinelearningintranslation AT sawyerdavidb machinelearningintranslation |