How single-language text can make AI translators better
AI that translates needs lots of examples, but paired sentences in two languages are rare.
We used lots of text in one language to teach a translator and it worked, the system learned patterns it didn’t know before and gave better translations in hard spots.
The gains were biggest for low-resource languages where little bilingual data exists, like Turkish-English and Chinese-English chat messages improved, some cases showing clear lift.
The same idea also helps when lots of bilingual data is available, so it’s not only for small languages, it helps big ones too.
The approach is easy to add, needs no extra labels, and it make a real difference to everyday texts people send.
You’ll see fewer odd phrases and clearer meaning because the model saw many more examples of how people speak.
Think of translation AI learning from many single-language books instead of only matched sentence pairs.
It seems to work, and it’s ready for use now.
Read article comprehensive review in Paperium.net:
On Using Monolingual Corpora in Neural Machine Translation
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