Extreme Adaptation for Personalized Neural Machine Translation
meeting of the association for computational linguistics, 2018.
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
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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