Productivity and quality when editing machine translation and translation memory outputs: an empirical analysis of English to Welsh translation

Screen, Benjamin

doi:10.1515/scp-2017-0007

Cited by 11 publications

(5 citation statements)

References 12 publications

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“…Only three participants out of 64 were slightly satisfied. This overall positive evaluation of the MTPE subtitles is consistent with the results of previous research that investigated the relationship between MTPE, human translation, and quality in different domains (Carl et al, 2011;Daems et al, 2013;García, 2011;Guerberof Arenas, 2008;Läubli et al, 2013;Plitt & Masselot, 2010;Screen, 2017;Skadiņš et al, 2011).…”

Section: Percentagesupporting

confidence: 90%

Quality assessment of machine-translated post-edited subtitles: an analysis of Brazilian translators’ perceptions

Koglin,

Moura,

Matos

et al. 2023

LANS-TTS

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This study focuses on the translation product in the form of subtitles and, in particular, investigates the perceived quality of machine-translated post-edited interlingual subtitles. Basing this study on data collected from Brazilian professional translators, we analyse whether the use of machine translation has an impact on the perceived quality and acceptability of interlingual subtitles. We also examine those technical parameters and linguistic aspects that the study participants considered troublesome and whether translators would report any evidence that the subtitles were machine-translated. Sixty-eight Brazilian translators volunteered to participate in this study. They completed a questionnaire, watched a movie trailer with post-edited subtitles, and then assessed the quality of the subtitles by rating them using a Likert-type scale. Finally, the participants responded to a written verbal protocol. Our analysis relies on the qualitative and quantitative data provided by the participants. The qualitative and quantitative data from the protocol answers were triangulated with the quantitative results from the Likert-type scale. IBM SPSS Statistics was used for statistical analysis. Our findings show that 28.1% of the translators assessed the post-edited subtitles as very satisfactory whereas 48.4% judged them as satisfactory. These results seem to be an indication of the acceptability of post-editing machine-translated subtitles as they find support in the written verbal protocol answers. Regarding the technical parameters, 43% of the participants reported no issues whereas 22.8% commented on the font colour and the font size. When asked about any linguistic aspects that could have disturbed the viewing experience, 64.1% of them reported that no linguistic issues were evident in the subtitles. A small percentage of the participants reported some linguistic problems, such as a lack of accuracy, the use of literal translation, and unnatural subtitles. Despite this apparent linguistic evidence of machine translation, no participant explicitly affirmed that the subtitles were machine-translated and post-edited.

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Section: Percentagesupporting

confidence: 90%

Quality assessment of machine-translated post-edited subtitles: an analysis of Brazilian translators’ perceptions

Koglin,

Moura,

Matos

et al. 2023

LANS-TTS

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“…In order to improve the speed of traditional stand-alone translation algorithms, there are some optimizations for language model query performance. Screen proposed the use of Bloom filters to handle very large-scale language models [ 11 ]. But this method compresses the amount of data, which reduces the accuracy.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Analysis of Machine Translation and Post‐Translation Editing Ability Using Semantic Information Entropy Technology

Zou

2022

Journal of Environmental and Public Health

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Large-scale corpus application has presented MT with new opportunities as well as challenges in recent years. This study investigates MT and post-translation editing capability using AI technology. The grammar rules of the target language are first examined. Then, a significant amount of data on semantic information entropy are projected, and the semantic Gaussian marginal rectangular window function is obtained. The semantic correlation factors of words are added to the text information entropy and information gain, and the nonlinear spectral properties of adaptive matching semantics are obtained. In this way, it corrects the significant flaw in the way semantic features are extracted using conventional techniques. In order to speed up MT and enhance translation quality, this study proposes automatic post-translation editing to filter those common MT errors that occur frequently and regularly. According to the experimental findings, word translation and segmentation accuracy can both reach 95.27 and 93.12 percent, respectively. In terms of language translation, this approach is accurate and trustworthy. I hope it will serve as a useful source for subsequent research.

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“…Technologies like machine translation can significantly reduce the time and costs of translation [1] [2] in public sector institutions. In many scenarios, machine translation is the only feasible way to provide access to e-government services in multiple languages.…”

Section: Introductionmentioning

confidence: 99%

Language Technology Platform for Public Administration

Skadiņš¹,

Pinnis²,

Vasiļevskis³

et al. 2020

Frontiers in Artificial Intelligence and Applications

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The paper describes the Latvian e-government language technology platform HUGO.LV. It provides an instant translation of text snippets, formatting-rich documents and websites, an online computer-assisted translation tool with a built-in translation memory, a website translation widget, speech recognition and speech synthesis services, a terminology management and publishing portal, language data storage, analytics, and data sharing functionality. The paper describes the motivation for the creation of the platform, its main components, architecture, usage statistics, conclusions, and future developments. Evaluation results of language technology tools integrated in the platform are provided.

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Productivity and quality when editing machine translation and translation memory outputs: an empirical analysis of English to Welsh translation

Cited by 11 publications

References 12 publications

Quality assessment of machine-translated post-edited subtitles: an analysis of Brazilian translators’ perceptions

Quality assessment of machine-translated post-edited subtitles: an analysis of Brazilian translators’ perceptions

[Retracted] Analysis of Machine Translation and Post‐Translation Editing Ability Using Semantic Information Entropy Technology

Language Technology Platform for Public Administration

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