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Article

The Role of Machine Translation Quality Estimation in the Post-Editing Workflow

1
Hertie School Data Science Lab., 10117 Berlin, Germany
2
Centre of Translation Studies, University of Surrey, Guildford GU2 7XH, UK
3
RWS Language Weaver, Dublin, Ireland
4
Rochester Institute of Technology, Department of Computer Science, Rochester, NY 14623, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Antony Bryant
Informatics 2021, 8(3), 61; https://doi.org/10.3390/informatics8030061
Received: 15 July 2021 / Revised: 31 August 2021 / Accepted: 6 September 2021 / Published: 14 September 2021
As Machine Translation (MT) becomes increasingly ubiquitous, so does its use in professional translation workflows. However, its proliferation in the translation industry has brought about new challenges in the field of Post-Editing (PE). We are now faced with a need to find effective tools to assess the quality of MT systems to avoid underpayments and mistrust by professional translators. In this scenario, one promising field of study is MT Quality Estimation (MTQE), as this aims to determine the quality of an automatic translation and, indirectly, its degree of post-editing difficulty. However, its impact on the translation workflows and the translators’ cognitive load is still to be fully explored. We report on the results of an impact study engaging professional translators in PE tasks using MTQE. To assess the translators’ cognitive load we measure their productivity both in terms of time and effort (keystrokes) in three different scenarios: translating from scratch, post-editing without using MTQE, and post-editing using MTQE. Our results show that good MTQE information can improve post-editing efficiency and decrease the cognitive load on translators. This is especially true for cases with low MT quality. View Full-Text
Keywords: machine translation quality estimation; post-editing machine translation quality estimation; post-editing
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MDPI and ACS Style

Béchara, H.; Orăsan, C.; Parra Escartín, C.; Zampieri, M.; Lowe, W. The Role of Machine Translation Quality Estimation in the Post-Editing Workflow. Informatics 2021, 8, 61. https://doi.org/10.3390/informatics8030061

AMA Style

Béchara H, Orăsan C, Parra Escartín C, Zampieri M, Lowe W. The Role of Machine Translation Quality Estimation in the Post-Editing Workflow. Informatics. 2021; 8(3):61. https://doi.org/10.3390/informatics8030061

Chicago/Turabian Style

Béchara, Hannah, Constantin Orăsan, Carla Parra Escartín, Marcos Zampieri, and William Lowe. 2021. "The Role of Machine Translation Quality Estimation in the Post-Editing Workflow" Informatics 8, no. 3: 61. https://doi.org/10.3390/informatics8030061

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