Horses for Courses! Which MT for which language?
As the localisation industry continues to embrace AI, it’s clear that not all machine ‘translation (MT) systems are created equal—especially when it comes to handling certain languages or topics.
We’ve discussed before how content type (e.g.marketing, pharmaceutical, or training materials) can influence MT performance. But today, let’s talk languages, specifically Slavic languages and the unique challenges they present for MT.
The Issue: Gender-specific verb endings
In many Slavic languages like Russian, Polish, and Czech, verbs in the past tense must align with the gender of the subject. For example:
- In Polish, a male speaker says, “Poszedłem do sklepu” (“I went to the store”), while a female speaker says, “Poszłam do sklepu.”
- Without knowing the gender of Alex, translating “Alex went home” in Russian might produce the masculine “Алекс пошёл домой” instead of the correct feminine form “Алекс пошла домой.”
Why does MT struggle?
- English (and similar languages) lack explicit gender cues, leaving MT systems without crucial context.
- Many systems default to the masculine form, leading to errors that can be problematic in formal or sensitive contexts.
What’s the solution?
- Developing AI that understands context across sentences.
- Allowing users to specify gender preferences during translation.
- Training systems with more diverse datasets that include gender variations.
At #Accentua, we’re passionate about preserving cultural and grammatical nuances in translation. If you’re targeting Eastern European markets, expanding teams in cost-effective locations, or encountering similar issues with machine translations, we’d love to support you.
#Localisation #MachineTranslation #SlavicLanguages #AI #GenderSpecificLanguages