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.
Highlights
The Directorate-General for Translation (DGT) of the European Commission translates written texts into and out of the EU’s 24 official languages
At project creation, the source text is sent to the machine translation (MT) engine and the MT output is stored as a translation memory exchange (TMX) file, the standard exchange format for translation memories
The study shows that on average, machine translation provides measurable benefits in real-life translation scenarios, and that these benefits are more consistent for neural machine translation (NMT)-FI than for SMT-FR. This complements research findings on the assessment of neural vs. statistical machine translation systems, which found that neural translation systems seem to provide more consistently useful output than statistical ones
Summary
The Directorate-General for Translation (DGT) of the European Commission translates written texts into and out of the EU’s 24 official languages. As the largest institutional translation service in the world, DGT has a long tradition in using machine translation (MT). In the 1980s, a rule-based machine translation (RBMT) system (based on technology of Systran) was already operational [1,2]. A phrase-based statistical machine translation (PBSMT) system based on Moses [3], was introduced in. From November 2017 onward, this system has gradually been replaced by a new system based on neural machine translation (NMT). Machine translation is offered as a translation aid to all translators and is fully integrated in the translation workflow. All DGT translators work within SDL Trados Studio in which basically two TMX files are imported: a TMX file which contains the retrieved matches from the huge central translation memory (EURAMIS) and a TMX file which contains the MT suggestions
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