Faster, Smarter Translation That Reads Like a Human
This new system learns to turn one sentence into another by using two simple parts: one part reads the input and the other part writes the output, and they work together so the timing stays right.
Because it looks at whole chunks not one word at a time, it can be faster and handle long sentences without forgetting earlier words.
It learns from letters and words, so it can do really good character-level language work — think translations that keep small details.
The result is translations that feel more natural, they come quicker and use less memory, so phones or websites can run them easier.
You might notice it handles tricky reordering better, so phrases end up where they should be, even when languages differ a lot.
This isn't perfect yet but it shows how machines can read and write in ways that match what people expect, and it's a step toward truly real-time and more accurate translations you'll actually want to use.
Read article comprehensive review in Paperium.net:
Neural Machine Translation in Linear Time
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