Machine translation has become a valuable and complex tool in the arsenal of localization experts. And as with all tools, it’s all about having the right people to use them in order to bring out the best results possible.
Which machine translation engines perform better in different domains?
Which machine translation engines perform better in different domains?
The Intento report on the State of Machine Translation 2022 brought with it a wealth of insight on how current machine translation engines perform. And while MT customization is the gold standard when it comes to domain-specific translations, it’s interesting to note how even the stock models are performing in different domains.
In this article, we will take a look at how well MT models perform across different domains,and take note of trends and significant patterns in the data from the Intento report. Let’s dive in.
Report overview
Colloquial
Education
Entertainment
Financial
General
Healthcare
Hospitality
IT
Legal
In this article, we will take a look at the top-performing MT latvia mobile database engines across different languages in each domain. The report notes 16 leaders in the same tier, across all domains as measured by normalized COMET scores.
What about custom models?
Most of the MT engines under evaluation offer custom models that are adapted to different domains. But it should be noted that the report only evaluates the stock models—that is, without any customization—of these MT engines, and as such are trained on generic data that isn’t concentrated in any particular domain.
Custom models are generally the preferred option when it comes to domain-specific translations, but there are cases where general machine translation is adequate. Choosing an MT engine that performs well in certain domains also in all likelihood makes it easier to train them further.
To learn more about the difference between stock and custom MT models, read our article: Custom vs stock models in machine translation: Which is better?