The Challenges of Teaching and Assessing Technical Translation in an Era of Neural Machine Translation

Teaching translation in higher education has undeniably been impacted by the innovations brought about by machine translation (MT), more particularly neural machine translation (NMT). This influence has become significantly more noticeable in recent years, as NMT technology progresses hand in hand with artificial intelligence. A case study supported by a questionnaire conducted among translation students (bachelor’s and master’s programmes at ISCAP) probed the degree of student satisfaction with CAT tools and revealed that they favour the use of MT in their translation practices, focusing their work on post-editing tasks rather than exploring other translation strategies and complementary resources. Although MT cannot be disregarded in translation programmes, as machine-generated translations make up an increasingly larger amount of a professional translator’s output, the widespread use of MT by students poses new challenges to translators’ training, since it becomes more difficult to assess students’ level of proficiency. Translation teachers must not only adapt their classroom strategies to accommodate these current translation strategies (NMT) but also, as intended by this study, find new, adequate methods of training and assessing students that go beyond regular translation assignments while still ensuring that students acquire the proper translation competence. Thus, as the use of NMT makes it considerably more challenging to assess a student’s level of translation competence, it is necessary to introduce other activities that not only allow students to acquire and develop their translation competence as defined in the EMT (European Masters in Translation) framework but also enable teachers to assess students more objectively. Hence, this article foregrounds a set of activities usually regarded as “indirect tasks” for technical translation courses that hopefully results in the development of student translation skills and competence, as well as provides more insights for teachers on how to more objectively assess students. It is possible, then, to conclude that these activities, such as different types of paraphrasing and error-detection tasks, may have the potential to encourage creative thinking and problem-solving strategies, giving teachers more resources to assess students’ level of translation competence. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Célia Tavares [verfasserIn] Laura Tallone [verfasserIn] Luciana Oliveira [verfasserIn] Sandra Ribeiro [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	technical translation neural machine translation translator competence translation teaching translation assessment

Übergeordnetes Werk:	In: Education Sciences - MDPI AG, 2012, 13(2023), 6, p 541
Übergeordnetes Werk:	volume:13 ; year:2023 ; number:6, p 541

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/educsci13060541

Katalog-ID:	DOAJ094170371

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allfieldsSound	10.3390/educsci13060541 doi (DE-627)DOAJ094170371 (DE-599)DOAJ527df65164ae42a5a315234a8e6831b8 DE-627 ger DE-627 rakwb eng Célia Tavares verfasserin aut The Challenges of Teaching and Assessing Technical Translation in an Era of Neural Machine Translation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Teaching translation in higher education has undeniably been impacted by the innovations brought about by machine translation (MT), more particularly neural machine translation (NMT). This influence has become significantly more noticeable in recent years, as NMT technology progresses hand in hand with artificial intelligence. A case study supported by a questionnaire conducted among translation students (bachelor’s and master’s programmes at ISCAP) probed the degree of student satisfaction with CAT tools and revealed that they favour the use of MT in their translation practices, focusing their work on post-editing tasks rather than exploring other translation strategies and complementary resources. Although MT cannot be disregarded in translation programmes, as machine-generated translations make up an increasingly larger amount of a professional translator’s output, the widespread use of MT by students poses new challenges to translators’ training, since it becomes more difficult to assess students’ level of proficiency. Translation teachers must not only adapt their classroom strategies to accommodate these current translation strategies (NMT) but also, as intended by this study, find new, adequate methods of training and assessing students that go beyond regular translation assignments while still ensuring that students acquire the proper translation competence. Thus, as the use of NMT makes it considerably more challenging to assess a student’s level of translation competence, it is necessary to introduce other activities that not only allow students to acquire and develop their translation competence as defined in the EMT (European Masters in Translation) framework but also enable teachers to assess students more objectively. Hence, this article foregrounds a set of activities usually regarded as “indirect tasks” for technical translation courses that hopefully results in the development of student translation skills and competence, as well as provides more insights for teachers on how to more objectively assess students. It is possible, then, to conclude that these activities, such as different types of paraphrasing and error-detection tasks, may have the potential to encourage creative thinking and problem-solving strategies, giving teachers more resources to assess students’ level of translation competence. technical translation neural machine translation translator competence translation teaching translation assessment Education L Laura Tallone verfasserin aut Luciana Oliveira verfasserin aut Sandra Ribeiro verfasserin aut In Education Sciences MDPI AG, 2012 13(2023), 6, p 541 (DE-627)737287543 (DE-600)2704213-3 22277102 nnns volume:13 year:2023 number:6, p 541 https://doi.org/10.3390/educsci13060541 kostenfrei https://doaj.org/article/527df65164ae42a5a315234a8e6831b8 kostenfrei https://www.mdpi.com/2227-7102/13/6/541 kostenfrei https://doaj.org/toc/2227-7102 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2086 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 6, p 541
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author	Célia Tavares
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title_sort	challenges of teaching and assessing technical translation in an era of neural machine translation
title_auth	The Challenges of Teaching and Assessing Technical Translation in an Era of Neural Machine Translation
abstract	Teaching translation in higher education has undeniably been impacted by the innovations brought about by machine translation (MT), more particularly neural machine translation (NMT). This influence has become significantly more noticeable in recent years, as NMT technology progresses hand in hand with artificial intelligence. A case study supported by a questionnaire conducted among translation students (bachelor’s and master’s programmes at ISCAP) probed the degree of student satisfaction with CAT tools and revealed that they favour the use of MT in their translation practices, focusing their work on post-editing tasks rather than exploring other translation strategies and complementary resources. Although MT cannot be disregarded in translation programmes, as machine-generated translations make up an increasingly larger amount of a professional translator’s output, the widespread use of MT by students poses new challenges to translators’ training, since it becomes more difficult to assess students’ level of proficiency. Translation teachers must not only adapt their classroom strategies to accommodate these current translation strategies (NMT) but also, as intended by this study, find new, adequate methods of training and assessing students that go beyond regular translation assignments while still ensuring that students acquire the proper translation competence. Thus, as the use of NMT makes it considerably more challenging to assess a student’s level of translation competence, it is necessary to introduce other activities that not only allow students to acquire and develop their translation competence as defined in the EMT (European Masters in Translation) framework but also enable teachers to assess students more objectively. Hence, this article foregrounds a set of activities usually regarded as “indirect tasks” for technical translation courses that hopefully results in the development of student translation skills and competence, as well as provides more insights for teachers on how to more objectively assess students. It is possible, then, to conclude that these activities, such as different types of paraphrasing and error-detection tasks, may have the potential to encourage creative thinking and problem-solving strategies, giving teachers more resources to assess students’ level of translation competence.
abstractGer	Teaching translation in higher education has undeniably been impacted by the innovations brought about by machine translation (MT), more particularly neural machine translation (NMT). This influence has become significantly more noticeable in recent years, as NMT technology progresses hand in hand with artificial intelligence. A case study supported by a questionnaire conducted among translation students (bachelor’s and master’s programmes at ISCAP) probed the degree of student satisfaction with CAT tools and revealed that they favour the use of MT in their translation practices, focusing their work on post-editing tasks rather than exploring other translation strategies and complementary resources. Although MT cannot be disregarded in translation programmes, as machine-generated translations make up an increasingly larger amount of a professional translator’s output, the widespread use of MT by students poses new challenges to translators’ training, since it becomes more difficult to assess students’ level of proficiency. Translation teachers must not only adapt their classroom strategies to accommodate these current translation strategies (NMT) but also, as intended by this study, find new, adequate methods of training and assessing students that go beyond regular translation assignments while still ensuring that students acquire the proper translation competence. Thus, as the use of NMT makes it considerably more challenging to assess a student’s level of translation competence, it is necessary to introduce other activities that not only allow students to acquire and develop their translation competence as defined in the EMT (European Masters in Translation) framework but also enable teachers to assess students more objectively. Hence, this article foregrounds a set of activities usually regarded as “indirect tasks” for technical translation courses that hopefully results in the development of student translation skills and competence, as well as provides more insights for teachers on how to more objectively assess students. It is possible, then, to conclude that these activities, such as different types of paraphrasing and error-detection tasks, may have the potential to encourage creative thinking and problem-solving strategies, giving teachers more resources to assess students’ level of translation competence.
abstract_unstemmed	Teaching translation in higher education has undeniably been impacted by the innovations brought about by machine translation (MT), more particularly neural machine translation (NMT). This influence has become significantly more noticeable in recent years, as NMT technology progresses hand in hand with artificial intelligence. A case study supported by a questionnaire conducted among translation students (bachelor’s and master’s programmes at ISCAP) probed the degree of student satisfaction with CAT tools and revealed that they favour the use of MT in their translation practices, focusing their work on post-editing tasks rather than exploring other translation strategies and complementary resources. Although MT cannot be disregarded in translation programmes, as machine-generated translations make up an increasingly larger amount of a professional translator’s output, the widespread use of MT by students poses new challenges to translators’ training, since it becomes more difficult to assess students’ level of proficiency. Translation teachers must not only adapt their classroom strategies to accommodate these current translation strategies (NMT) but also, as intended by this study, find new, adequate methods of training and assessing students that go beyond regular translation assignments while still ensuring that students acquire the proper translation competence. Thus, as the use of NMT makes it considerably more challenging to assess a student’s level of translation competence, it is necessary to introduce other activities that not only allow students to acquire and develop their translation competence as defined in the EMT (European Masters in Translation) framework but also enable teachers to assess students more objectively. Hence, this article foregrounds a set of activities usually regarded as “indirect tasks” for technical translation courses that hopefully results in the development of student translation skills and competence, as well as provides more insights for teachers on how to more objectively assess students. It is possible, then, to conclude that these activities, such as different types of paraphrasing and error-detection tasks, may have the potential to encourage creative thinking and problem-solving strategies, giving teachers more resources to assess students’ level of translation competence.
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ094170371</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240413030806.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240413s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.3390/educsci13060541</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ094170371</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ527df65164ae42a5a315234a8e6831b8</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Célia Tavares</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="4"><subfield code="a">The Challenges of Teaching and Assessing Technical Translation in an Era of Neural Machine Translation</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Teaching translation in higher education has undeniably been impacted by the innovations brought about by machine translation (MT), more particularly neural machine translation (NMT). This influence has become significantly more noticeable in recent years, as NMT technology progresses hand in hand with artificial intelligence. A case study supported by a questionnaire conducted among translation students (bachelor’s and master’s programmes at ISCAP) probed the degree of student satisfaction with CAT tools and revealed that they favour the use of MT in their translation practices, focusing their work on post-editing tasks rather than exploring other translation strategies and complementary resources. Although MT cannot be disregarded in translation programmes, as machine-generated translations make up an increasingly larger amount of a professional translator’s output, the widespread use of MT by students poses new challenges to translators’ training, since it becomes more difficult to assess students’ level of proficiency. Translation teachers must not only adapt their classroom strategies to accommodate these current translation strategies (NMT) but also, as intended by this study, find new, adequate methods of training and assessing students that go beyond regular translation assignments while still ensuring that students acquire the proper translation competence. Thus, as the use of NMT makes it considerably more challenging to assess a student’s level of translation competence, it is necessary to introduce other activities that not only allow students to acquire and develop their translation competence as defined in the EMT (European Masters in Translation) framework but also enable teachers to assess students more objectively. Hence, this article foregrounds a set of activities usually regarded as “indirect tasks” for technical translation courses that hopefully results in the development of student translation skills and competence, as well as provides more insights for teachers on how to more objectively assess students. It is possible, then, to conclude that these activities, such as different types of paraphrasing and error-detection tasks, may have the potential to encourage creative thinking and problem-solving strategies, giving teachers more resources to assess students’ level of translation competence.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">technical translation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">neural machine translation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">translator competence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">translation teaching</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">translation assessment</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Education</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield 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The Challenges of Teaching and Assessing Technical Translation in an Era of Neural Machine Translation

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