On the Properties of Neural Machine Translation: Encoder-Decoder Approaches

Understand

Neural machine translation is a relatively new approach to statistical machine translation based purely on neural networks.

• The neural machine translation models often consist of an encoder and a decoder.
• The encoder extracts a fixed-length representation from a variable-length input sentence, and the decoder generates a correct translation from this representation.
• In this paper, we focus on analyzing the properties of the neural machine translation using two models; RNN Encoder--Decoder and a newly proposed gated recursive convolutional neural network.