对译后编辑人员特点和实践的思考。

Blagodarna, Olena

doi:10.5565/rev/tradumatica.198

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…Although the results are hard to compare due to different scopes and target groups of conducted research, the findings of this study are in line with those of previous research targeting multiple countries worldwide. A survey by Blagodarna (2018) has addressed 26 countries, including Lithuania, and found that the frequency of performing post-editing was varied between "never" to "very often" but a tendency was noticed for the answer "sometimes" to prevail. In our case, freelancers were more prone to do post-editing than language service providers (i.e., companies) on a day to day basis.…”

Section: Discussionmentioning

confidence: 99%

Machine Translation for Post-Editing Practices

Povilaitienė

Kasperẹ

2022

ctld

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It has been proven in many quality-focused studies that machine translation output in some language pairs is still far from publishable (Koponen, 2016). Even so, post-editing has become a daily practice among translators and translation service providers, especially with language pairs where machine translation demonstrates good human parity. The fast development of machine translation and its quality improvement have led to a growing demand of post-editors. This study attempts to evaluate the quality of the most popular machine translation tools for the Lithuanian language in order to find the correlation between the results of automatic quality estimation (i.e., the BLUE score), human / manual evaluation of machine translation output quality following the multidimensional quality metrics (MQM) and the most common machine translation engines used by freelancers and language service providers. The conclusions are based on the findings of a survey and the automatic vs human / manual machine translation quality analysis. The findings demonstrate and support previous research that automatic machine translation quality estimation may not be taken for granted. Human / manual machine translation quality evaluation is still a better indicator whether a machine translation tool fits the purpose of translation. The study brings to the fore some insightful findings that may be beneficial for translator and post-editor trainers from the pedagogical perspective as well as for translation industry from the practical perspective.

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Section: Discussionmentioning

confidence: 99%

Machine Translation for Post-Editing Practices

Povilaitienė

Kasperẹ

2022

ctld

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“…In the extensive literature on the subject of translators' attitudes towards MTPE (Blagodarna 2018;Cadwell et al 2018;Ginovart et al 2020;Ragni and Vieira 2022;Vieira 2018;Teixiera and O'Brien 2018, among others) there is little mention of how translators perceive MTPE guidelines or how these affect the way the task is performed. Factors affecting attitudes refer to price, productivity, effort or cognitive load, but guidelines are seldom mentioned.…”

Section: Translators' Expectations Towards Mtpe Guidelinesmentioning

confidence: 99%

Re-thinking Machine Translation Post-Editing Guidelines

Rico Pérez

2024

JoSTrans

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Machine Translation Post-Editing (MTPE) is a challenging task. It frequently creates tension between what the industry expects in terms of quality and what translators are willing to deliver as an end product. Conventional approaches to MTPE take as a point of departure the distinction between light and full MPTE, but the division gets blurred when implemented in an actual MTPE project where translators find difficulties in differentiating between essential and preferential changes. At the time MTPE guidelines were designed, the role of the human translator in the MT process was perceived as ancillary, a view inherited from the first days of MT research aiming at the so-called Fully Automatic High Quality Machine Translation (FAHQMT). My proposal challenges the traditional division of MTPE levels and presents a new way of looking at MTPE guidelines. In view of the latest developments in neural machine translation and the higher quality level of its output, it is my contention that the traditional division of MTPE levels is no longer valid. In this contribution I advance a proposal for redefining MTPE guidelines in the framework of an ecosystem specifically designed for this purpose.

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“…Ever since Google's introduction of neural Machine Translation (MT) in 2016 [Wu et al, 2016], we have seen an increase in the quality of MT solutions and a wider acceptance of its use by both the general population globally and the translation industry [Blagodarna, 2018]. As was to be expected, these developments have also had a considerable effect on translator training programmes.…”

Section: Introductionmentioning

confidence: 99%

The use of MT by undergraduate translation students for different learning tasks

Bindels¹,

Pluymaekers²

2022

Journal of Data Mining &Amp; Digital Humanities

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This study investigates the use of MT by first-, second-and third-year translation students for different learning tasks. The results of a self-report survey suggest that most students make regular use of MT, although the frequency of use decreases as they progress through their studies, particularly for reading tasks. Our results highlight the need for a comprehensive approach to the development of MT knowledge and PE skills among undergraduate students, using the concept of MT literacy as a stepping stone.

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对译后编辑人员特点和实践的思考。

Cited by 3 publications

References 11 publications

Machine Translation for Post-Editing Practices

Machine Translation for Post-Editing Practices

Re-thinking Machine Translation Post-Editing Guidelines

The use of MT by undergraduate translation students for different learning tasks

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