Testing post-editing guidelines: how translation trainees interpret them and how to tailor them for translator training purposes

Flanagan, Marian; Christensen, T.

doi:10.1080/1750399x.2014.936111

Cited by 35 publications

(16 citation statements)

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“…The latest trend in the language industry is to add MT to TM suites (Koby 2013), which we will refer to as MT-assisted TM, or to use an MT engine (typically built in-house) to pre-translate a source text, which is then post-edited (e.g. Flanagan/Christensen 2014;. Post-editing (PE) is defined as the task of editing, modifying and/or correcting pre-translated texts that have been processed by an MT system from a source language into a target language (for an overview, see Flanagan/Christensen 2014: 257).…”

mentioning

confidence: 99%

Mapping Translation Technology Research in Translation Studies. An Introduction to the Thematic Section

Christensen¹,

Flanagan²,

Schjoldager³

2017

HJLCB

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confidence: 99%

Mapping Translation Technology Research in Translation Studies. An Introduction to the Thematic Section

Christensen¹,

Flanagan²,

Schjoldager³

2017

HJLCB

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“…Depraetere (2010), for instance, studied a corpus of texts that had been post-edited by 10 students in order to discover which post-editing guidelines she needed to most emphasise during translation training. Along the same lines, and using retrospective interviews with three students as the main data elicitation technique, Flanagan and Christensen (2014) aimed to discover how students interpret TAUS post-editing guidelines, with the ultimate goal of identifying potential competence gaps and address them by adapting that type of industry material for training purposes. As a result of the study, a new set of guidelines was created, including additional clarifying information to help trainees interpreting them.…”

Section: Research Approaches and Key Findingsmentioning

confidence: 99%

“…Thus, some undergraduate programmes may cover more advanced aspects of CAT than postgraduate ones, if they offer more than one CAT course. The type of technologies included may vary as well, with recent studies demonstrating a stronger prevalence of machine translation-related contents in the syllabus(Depraetere 2010;Flanagan and Christensen 2014;Kenny and Doherty 2014).…”

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Translation and technology

O’Brien¹,

Vázquez²

2019

The Routledge Handbook of Translation and Education

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INTRODUCTIONTranslation Technology, Translation Tools, Translation Environment Tools (or TEnTs) andComputer-Aided Translation (or CAT) are frequently used as synonyms. In its broadest sense translation technology is understood to include a large array of computer tools that help translators do their jobs, including word processors, spell, style and grammar checkers, the World Wide Web, corpus compilation and analysis tools, terminology management tools, translation memory tools (TM), translation management systems (TMS) and machine translation (MT). Bowker (2002 p. 7) arranges these tools along a continuum from less automated (human translation) to more automated (machine translation), with CAT positioned in the middle. In the human translation rubric, she includes, for example, word processors and spell checkers, and in CAT, translation memory, corpus analysis tools, terminology management systems, data capture tools, localisation and web-page translation tools, and diagnostic tools. The MT rubric includes only machine translation systems. This extensive list is evidence of how technologised the translation profession is. For the sake of clarity throughout this chapter, we refer to translation technology broadly speaking as Computer-Aided Translation -CAT. Where machine translation is specifically meant, we use that term.Likewise, when we want to draw attention to a specific type of tool, we use the exact term, e.g. terminology management tool or translation memory (TM).Over the last decades, parts of the translation profession have moved from deploying hardly any technology (in the 1980s), to embracing of Translation Memory and Terminology Management (in the 1990s), to the introduction of MT and its convergence with TM tools.Industry commentators predict a move towards Fully-Automatic Useful Translation -or FAUT -by 2030 (Massardo and van der Meer 2017). Currently, translators are still expected to produce high quality translation and that is often done with the aid of CAT tools. For this reason, it is important that translation technology is firmly embedded in the translator training curriculum. As Bowker (2002) points out, knowledge of CAT increases employability of graduates, but there are additional benefits to the teaching of translation technology: students can become more attuned to their own and to peer approaches to translation, data is created for research into translation, and reflections on the impact of technology on process, product and profession can be generated (ibid).This chapter will first discuss early considerations on teaching translation technology. We select a subset of examples to give an overview of what these early considerations entailed. A number of formal translator competence models are examined so as to illustrate the place of translation technology in these models. Next, we provide a synopsis of research concerns relating to translation technology. Then, we review the pedagogical approaches adopted these days for CAT teaching before turning to conclusions, where we outline curre...

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“…Regarding empirical research in the classroom, we would like to mention Flanagan and Christensen (2014), who examined trainees' interpretations of post-editing guidelines provided by professional associations through tasks, reflective essays and interviews and conclude that students have difficulties understanding some of these guidelines. They therefore suggest tailoring post-editing guidelines to students' needs and consider that special attention should be paid to this area in translator education research (2014, 258): «One area requiring further investigation is the training of translators in PE, with particular focus on PE guidelines for training purposes.» Moorkens (2018) conducted an evaluation exercise with second year undergraduate students with little or no MT experience and participants at a PhD summer school «to empower the students, and help them understand the strengths and weaknesses of this new technology« (2018, 375).…”

Section: Research Regarding Mt In Translator Educationmentioning

confidence: 99%

La traducción automática en la formación de traductores

Schmidhofer

Mair

2018

CLINA

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With the improvement of free, next-generation machine translation software based on neural networks, Departments for Translation Studies are faced with the question of how to react to this new reality. Translator education programmes should include these and other tools that can complement human translation. However, in order to design activities that incorporate this software, we need data and experience that can serve as a starting point. For this purpose, we have conducted an experiment in a General Translation class and a Specialised Translation class. This article shows the results of this research, including both the quality of the translated text and the students' impressions.

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Testing post-editing guidelines: how translation trainees interpret them and how to tailor them for translator training purposes

Cited by 35 publications

References 10 publications

Mapping Translation Technology Research in Translation Studies. An Introduction to the Thematic Section

Mapping Translation Technology Research in Translation Studies. An Introduction to the Thematic Section

Translation and technology

La traducción automática en la formación de traductores

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