The final encoder consists of two stacked convolutional networks (Figure 1): CNN-a produces the encoder output z j to compute the attention scores a i , while the conditional input c i to the decoder is computed by summing the outputs of CNN-c, In practice, we found that two different CNNs resulted in better perplexity as well as BLEU compared to using a single one ( §5.3).
this did not cause a change in attention patterns nor did it improve translation accuracy.