Quantifying the Effect of Machine Translation in a High-Quality Human Translation Production Process
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 prof...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Informatics |
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| Online Access: | https://www.mdpi.com/2227-9709/7/2/12 |
| _version_ | 1797569813797666816 |
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| author | Lieve Macken Daniel Prou Arda Tezcan |
| author_facet | Lieve Macken Daniel Prou Arda Tezcan |
| author_sort | Lieve Macken |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T20:16:52Z |
| format | Article |
| id | doaj.art-e5ffea744f1f46728e1147795a362c90 |
| institution | Directory Open Access Journal |
| issn | 2227-9709 |
| language | English |
| last_indexed | 2024-03-10T20:16:52Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Informatics |
| spelling | doaj.art-e5ffea744f1f46728e1147795a362c902023-11-19T22:32:17ZengMDPI AGInformatics2227-97092020-04-01721210.3390/informatics7020012Quantifying the Effect of Machine Translation in a High-Quality Human Translation Production ProcessLieve Macken0Daniel Prou1Arda Tezcan2LT<sup>3</sup>, Language and Translation Technology Team, Ghent University, 9000 Ghent, BelgiumEuropean Commission Directorate-General for Translation, 1140 Brussels, BelgiumLT<sup>3</sup>, Language and Translation Technology Team, Ghent University, 9000 Ghent, BelgiumThis 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.https://www.mdpi.com/2227-9709/7/2/12machine translationcomputer-aided translationEuropean Commission (DGT)post-editingproductivity |
| spellingShingle | Lieve Macken Daniel Prou Arda Tezcan Quantifying the Effect of Machine Translation in a High-Quality Human Translation Production Process Informatics machine translation computer-aided translation European Commission (DGT) post-editing productivity |
| title | Quantifying the Effect of Machine Translation in a High-Quality Human Translation Production Process |
| title_full | Quantifying the Effect of Machine Translation in a High-Quality Human Translation Production Process |
| title_fullStr | Quantifying the Effect of Machine Translation in a High-Quality Human Translation Production Process |
| title_full_unstemmed | Quantifying the Effect of Machine Translation in a High-Quality Human Translation Production Process |
| title_short | Quantifying the Effect of Machine Translation in a High-Quality Human Translation Production Process |
| title_sort | quantifying the effect of machine translation in a high quality human translation production process |
| topic | machine translation computer-aided translation European Commission (DGT) post-editing productivity |
| url | https://www.mdpi.com/2227-9709/7/2/12 |
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