There is, however, no agreement about how much time can be saved through post-editing in practice (if any at all): While the industry reports on time savings around 40%, some academic studies suggest that time savings under actual working conditions are more likely to be between 0–20%. ...
In simple terms, machine translation refers to the use of computer software to translate text (either written or spoken) from one language to another. In practice, creating a machine that translates with the nuance and finesse of a human translator has proven tricky.Machine translation quality ...
Machine translation post-editing (MTPE) has become a common practice in translation industry, which calls much attention in academia. However, little research has been carried out to investigate students' cognitive and motivational individual differences in MTPE. The purpose of the present study was ...
Post-editing time as a measure ofcognitive effort. Koponen M,Aziz W,Ramos L, et al. Proceedings of the AMTA Workshop on PosteditingTechnology and Practice . 2012Koponen, M., Aziz, W., Ramos, L., and Specia, L. (2012). Post-editing time as a measure of cognitive effort . In AMT...
9 RegisterLog in Sign up with one click: Facebook Twitter Google Share on Facebook postediting Wikipedia (ˌpəʊstˈɛdɪtɪŋ) n (Journalism & Publishing) the act of editing after a piece of writing has been produced or printed by a machine ...
One thing’s for sure: you’ll getaccurate translationsfrom AI-powered machine translation. But it’s good practice to know the common mistakes that are found in its raw output. That way, you can easily look out for them and correct each one so that they don’t slip into the final pro...
In partic- ular, they are acquired from a single location in space, which makes them insufficient for walkthrough applications or ren- dering of large near-field sources. In practice, their use is mostly limited to the rendering of an overall ambiance. Be- sides, since no explicit position ...
The conclusion of the paper, however, is that, in practice, this combined approach still needs to be improved, with more reliable QE predictions and more robust APE models. 5 Neural post-editing tested in a commercial context So far, this article has focused on academic experiments with APE,...
2019.“Post-editing in Practice: Process, product and networks.” JosTrans: Journal of Specialised Translation 311: 2–13. Voigt, Rob, and Dan Jurafsky. 2012.“Towards a Literary Machine Translation: The Role of Referential Cohesion.” In Proceedings of the NAACL-HLT 2012 Workshop on ...
.2018. “What Level of Quality Can Neural Machine Translation Attain on Literary Text?” InTranslation Quality Assessment: From Principles to Practice, ed. byJoss Moorkens;Sheila Castilho;Federico Gaspari; andStephen Doherty, 263–287. Heidelberg: Springer. ...