Editors’ foreword to the special issue on human factors in neural machine translation

Castilho, Sheila; Gaspari, Federico; Moorkens, Joss; Popović, Maja; Toral, Antonio

doi:10.1007/s10590-019-09231-y

Cited by 10 publications

(9 citation statements)

References 16 publications

(8 reference statements)

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“…As neural machine translation is increasingly more capable of modelling how natural languages work, the traditional tasks of translators are being gradually replaced by new challenges (Castilho et al, 2019). Consequently, more emphasis is placed on pre-and post-editing (revision) skills and competences (Pym, 2013;Robert et al, 2017), enabling the production of higher quality and near human-made translations.…”

Section: Internetes Hivatkozásokmentioning

confidence: 99%

Untitled

2022

Porta Lingua

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Section: Internetes Hivatkozásokmentioning

confidence: 99%

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2022

Porta Lingua

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“…The corpus comprises 18 non-empirical items (16%), namely ten general discussion articles (e.g. Pym, 2019), two review articles (Chatzikoumi, 2019;Popescu-Belis, 2019), two editorial forewords (Way and Forcada, 2018;Castilho et al, 2019a), one meta evaluation (Fomicheva and Specia, 2019), one discussion-based MA thesis, (Tommasello, 2019), a set of lecture slides (Virpioja, 2018), and one power-point presentation (Moorkens, 2019). These non-empirical sources were included because of their theoretical relevance or how they presented potential implications of NMT uptake.…”

Section: Publication Typesmentioning

confidence: 99%

“…MT is now used 'raw' (i.e. unedited) in many use cases (Way, 2013), so NMT raises questions about the value and degree of human involvement in language translation (Castilho et al, 2019a). Relevant questions include whether this technical paradigm shift in MT has led to a shift in the translation profession and, if so, what is changing, how translators are affected, and what they can do to best navigate this fast-evolving landscape.…”

Section: Introductionmentioning

confidence: 99%

What has changed with neural machine translation? A critical review of human factors

Ragni

Vieira

2021

Perspectives

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In recent years, the push towards automation and translation productivity led to great efforts dedicated to the development of machine translation (MT) systems. Neural machine translation (NMT) represents the latest of these efforts. In this paper we present a critical review of human factors in NMT research with two goals: to provide a snapshot of research in NMT involving human stakeholders, and to appraise how professional translators have been included in discourses around NMT. We report four key findings. First, from translators' perspective, changes brought about by the neural paradigm are not as much to do with workflows, but rather with the NMT editing process and its specifics. Second, the majority of NMT research involving human stakeholders is directed towards advancing the state of MT development rather than ensuring the usefulness of NMT as a tool for professionals. Third, the review suggested overall narrow conceptualisations of translation productivity that were often based solely on measures of processing time or throughput.Fourth, it emerged that NMT investigations involving end-users are still relatively scarce. We present and discuss these findings, and make recommendations for future research on topics including the concept of productivity and the role of NMT as a professional tool.

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“…regular media stories about impending technological unemployment and leaps in quality, and while these are often overblown, the improvement in MT quality for general domain texts in language pairs for which there are large data sets available has been impressive. Evaluations have found increased fluency and lower numbers of errors, although these have not been associated with significant increases in productivity when NMT is used in the production of publishable texts (Castilho et al 2019). The increase in output quality and the media attention on NMT means that more translation agencies are offering products using NMT in response to client demand, and a wider variety of use cases are being found for raw and post-edited MT (Way 2018).…”

Section: Technological Contextmentioning

confidence: 99%

Translation in the neoliberal era

Moorkens¹

2020

The Routledge Handbook of Translation and Globalization

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Translation in the neoliberal era Joss MoorkensThis chapter considers the impact of neoliberalism on translation as a profession, exacerbated by the global economic crisis of 2007/2008 and consequent policies of austerity adopted in many jurisdictions. Employment conditions for translators, as with the general trend, have moved towards a 'vendor model' of freelance and contingent work. For some translators this has given them more control over their working lives, whereas for others is has been disempowering as they struggle with speed and productivity demands, the unilateral imposition of technologies, and constant downward pressure on price. Translation technology has afforded the possibility of cutting costs by leveraging previous translations within editing environments and of training MT systems. However, for translators this technology has had a dual effect, with some tools foregrounding usability and others more focused on data gathering.The industry has taken an increasingly pragmatic view of product and process quality, prioritising shareholder returns rather than social quality for translators, as proposed by Abdallah, and long-term, sustainable thinking. This will probably need to change if it is to retain talent and to encourage new and potential entrants.

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Editors’ foreword to the special issue on human factors in neural machine translation

Cited by 10 publications

References 16 publications

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What has changed with neural machine translation? A critical review of human factors

Translation in the neoliberal era

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