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EPISODE · Jan 2, 2017 · 18 MIN

Google Neural Machine Translation

from Linear Digressions · host Katie Malone

Recently, Google swapped out the backend for Google Translate, moving from a statistical phrase-based method to a recurrent neural network. This marks a big change in methodology: the tried-and-true statistical translation methods that have been in use for decades are giving way to a neural net that, across the board, appears to be giving more fluent and natural-sounding translations. This episode recaps statistical phrase-based methods, digs into the RNN architecture a little bit, and recaps the impressive results that is making us all sound a little better in our non-native languages.

Episode metadata supplied by the publisher feed · Published Jan 2, 2017

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Google Neural Machine Translation

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