Date: February 1, 2017
- Motivations for implementing neural network to improve accuracy and efficiency are the many successes of neural network application in other fields.
- Google announced late last year that it had applied machine learning to its Google translate service, resulting in a neural network capable of “zero-shot” translation.
- The team claimed they could implement the model without changing the core Translate model, which includes encoder, decoder and attention.
- This is presented as evidence of the neural network generating it’s own procedures for more efficient translation all by itself.
- Google Translate trajectory over the past 10 years is from a few languages to 103 supported languages, translating over 140 billion languages per day.
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