Issue #22 – Mixture Models in Neural MT
24 Jan19
Issue #22 – Mixture Models in Neural MT
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 perform well when translating software documentation or news articles, and vice versa, for example.
Why is this the case? Domain specific systems have lower vocabulary size, less ambiguities, reduced grammatical constructs and this lowers the chances of making mistakes. However, domain specific systems are inherently narrow in their applicability, and not suitable for a broader set of needs, as can often be the case in practice. In contrast to a domain specific system, a general (or generic) system is equally good at translating several domains but may not give the best translations on any one domain.
Can we combine the benefits of a domain specific system but train a generic system? Can we divide our corpora in several clusters and train many models and
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