Domain Adaptation


Author: Dr. Rohit Gupta, Sr. Machine Translation Scientist @ Iconic

It goes without saying that Neural Machine Translation has become state of the art in MT. However, one challenge we still face is developing a single general MT system which works well across a variety of different input types. As we know from long-standing research into domain adaptation, a system trained on patent data doesn’t...

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Author: Dr. Patrik Lambert, Machine Translation Scientist @ Iconic

Neural Machine Translation is known to be particularly poor at translating out-of-domain data. That is, an engine trained on generic data will be much worse at translating medical documents than an engine trained on medical data. It is much more sensitive to such differences than, say, Statistical MT. This problem is partially solved by domain...

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Author: Raj Nath Patel, Machine Translation Scientist @ Iconic

While Neural MT has raised the bar in terms of the quality of general purpose machine translation, it is still limited when it comes to more intricate or technical use cases. That is where domain adaptation -- the process of developing and adapting MT for specific industries, content types, and use cases -- has a big...

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