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By default, DeepL will segment the input that you present to the system:
So, if you send these 4 related segments, they will be treated as 'not related', which can lead to inconsistent translation of terms (here: Gesamtanlage):
Maßzeichnung der Gesamtanlage Gesamtanlage starten Gesamtanlage nach Sicherheitsstopp starten Beschickung der Gesamtanlage
This result matches the results of the 4 individual segments:
However, when you force DeepL to interpret the 4 segments as 1 segment, thus making them related, you'll get a better result ('Gesamtanlage' is translated consistently):
(On a side note: This approach has the disadvantage that the chapter titles in the example are interpreted as instructions.)
Of course, the peepl at DeepL are working hard on improving inter-segment consistency (which is considered to be the Holy Grail of MT).
In the meantime we could try to let CT offer several segments at the same time and re-segment the translated result to the TM.
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One idea that comes to mind is to save these chunks of 5000 characters and the DeepL-generated translations as bitexts and import these in CafeTran Espresso 2018.
I'd have to investigate if this approach would improve terminological consistency. ▲ Collapse
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