Neural Machine Translation using a Seq2Seq Architecture and Attention (ENG to POR)

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation [1]. Its strength comes from the fact that it learns the mapping directly from input text to associated output text. It has been proven to be more effective than traditional phrase-based machine translation, which requires much more effort to design the model. On the other hand, NMT models are costly to train, especially on large-scale translation datasets. They are also significantly slower at inference time due to the large number of parameters used.