Teach Computers to Fill Language Gaps — Fast and Simple
Many translation tools rely on big word lists, but those lists often miss words or phrases.
A new approach lets computers translate missing words by learning from lots of single-language text and only a little paired examples between languages.
It turn words into simple word maps so the computer can see which words are similar across tongues, then it learns a way to move between those maps.
The idea is plain, yet it helps the machine guess good matches even when no direct entry exists.
This method works with many languages because it makes few rules about grammar or writing, it only needs lots of monolingual text and some bilingual hints.
The result: faster growth of dictionaries and better suggestions when translating, even for rare words.
People will find translations that feel natural, and teams can fill gaps without endless manual work.
It help languages talk to each other more clear, and new word can be added much more easier than before.
Read article comprehensive review in Paperium.net:
Exploiting Similarities among Languages for Machine Translation
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