The concept of attention helped dramatically improve the seq_to_seq model. There was a lot of improvement and development on that concept since those two main papers on attention got released. One of them is the paper “Attention is All you Need.” The paper introduced a model called “Transformer” that uses only attention mechanisms to work with sequential data - no RNNs. The model they created is subject to parallelization and works very well with GPU. One of the main problems with RNN is that the algorithm is hardly parallelizable because before we can evaluate one time stamp in the encoder we need the previous one. You will see how we can easily parallelize the Transformer model. As before let’s consider the example of machine translation. As before the model consists from two parts:

• Encoders
• Decoders