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Recurrent neural networks (RNNs) have been performing well for learning tasks for several decades now. The most useful benefit they present for this paper is their ability to use contextual information when mapping between input and output sequences.
A deep neural network for machine translation implies the use of a sequence-to-sequence model, consisting of two RNNs: an encoder that processes the input and a decoder that generates the output.
To meaningfully assess the model’s performances, texts from a translation company and thoughts from skilled experts about specialized topics will be tested.