Extreme Adaptation for Personalized Neural Machine Translation

meeting of the association for computational linguistics, 2018.

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We further showed that the number of parameters specific to any person could be reduced to as low as 10 while still retaining better scores than a baseline for some language pairs, making it viable in a real world application with potentially millions of different users

Abstract:

Every person speaks or writes their own flavor of their native language, influenced by a number of factors: the content they tend to talk about, their gender, their social status, or their geographical origin. When attempting to perform Machine Translation (MT), these variations have a significant effect on how the system should perform t...More

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