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Quantifying the effect of machine translation in a high-quality human translation production process

Lieve Macken (UGent) , Daniel Prou and Arda Tezcan (UGent)
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Abstract
This paper studies the impact of machine translation (MT) on the translation workflow at the Directorate-General for Translation (DGT), focusing on two language pairs and two MT paradigms: English-into-French with statistical MT and English-into-Finnish with neural MT. We collected data from 20 professional translators at DGT while they carried out real translation tasks in normal working conditions. The participants enabled/disabled MT for half of the segments in each document. They filled in a survey at the end of the logging period. We measured the productivity gains (or losses) resulting from the use of MT and examined the relationship between technical effort and temporal effort. The results show that while the usage of MT leads to productivity gains on average, this is not the case for all translators. Moreover, the two technical effort indicators used in this study show weak correlations with post-editing time. The translators' perception of their speed gains was more or less in line with the actual results. Reduction of typing effort is the most frequently mentioned reason why participants preferred working with MT, but also the psychological benefits of not having to start from scratch were often mentioned.
Keywords
machine translation, computer-aided translation, European Commission (DGT), post-editing, productivity, LT3, EXPERIENCE

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MLA
Macken, Lieve, et al. “Quantifying the Effect of Machine Translation in a High-Quality Human Translation Production Process.” INFORMATICS-BASEL, vol. 7, no. 2, 2020, doi:10.3390/informatics7020012.
APA
Macken, L., Prou, D., & Tezcan, A. (2020). Quantifying the effect of machine translation in a high-quality human translation production process. INFORMATICS-BASEL, 7(2). https://doi.org/10.3390/informatics7020012
Chicago author-date
Macken, Lieve, Daniel Prou, and Arda Tezcan. 2020. “Quantifying the Effect of Machine Translation in a High-Quality Human Translation Production Process.” INFORMATICS-BASEL 7 (2). https://doi.org/10.3390/informatics7020012.
Chicago author-date (all authors)
Macken, Lieve, Daniel Prou, and Arda Tezcan. 2020. “Quantifying the Effect of Machine Translation in a High-Quality Human Translation Production Process.” INFORMATICS-BASEL 7 (2). doi:10.3390/informatics7020012.
Vancouver
1.
Macken L, Prou D, Tezcan A. Quantifying the effect of machine translation in a high-quality human translation production process. INFORMATICS-BASEL. 2020;7(2).
IEEE
[1]
L. Macken, D. Prou, and A. Tezcan, “Quantifying the effect of machine translation in a high-quality human translation production process,” INFORMATICS-BASEL, vol. 7, no. 2, 2020.
@article{8660184,
  abstract     = {This paper studies the impact of machine translation (MT) on the translation workflow at the Directorate-General for Translation (DGT), focusing on two language pairs and two MT paradigms: English-into-French with statistical MT and English-into-Finnish with neural MT. We collected data from 20 professional translators at DGT while they carried out real translation tasks in normal working conditions. The participants enabled/disabled MT for half of the segments in each document. They filled in a survey at the end of the logging period. We measured the productivity gains (or losses) resulting from the use of MT and examined the relationship between technical effort and temporal effort. The results show that while the usage of MT leads to productivity gains on average, this is not the case for all translators. Moreover, the two technical effort indicators used in this study show weak correlations with post-editing time. The translators' perception of their speed gains was more or less in line with the actual results. Reduction of typing effort is the most frequently mentioned reason why participants preferred working with MT, but also the psychological benefits of not having to start from scratch were often mentioned.},
  articleno    = {12},
  author       = {Macken, Lieve and Prou, Daniel and Tezcan, Arda},
  issn         = {2227-9709},
  journal      = {INFORMATICS-BASEL},
  keywords     = {machine  translation,computer-aided  translation,European  Commission  (DGT),post-editing,productivity,LT3,EXPERIENCE},
  language     = {eng},
  number       = {2},
  pages        = {19},
  title        = {Quantifying the effect of machine translation in a high-quality human translation production process},
  url          = {http://dx.doi.org/10.3390/informatics7020012},
  volume       = {7},
  year         = {2020},
}

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