A Lightweight Transformer with Convolutional Attention

Neural machine translation (NMT) goes through rapid development because of the application of various deep learning techs. Especially, how to construct a more effective structure of NMT attracts more and more attention. Transformer is a state-of-the-art architecture in NMT. It replies on the self-attention mechanism exactly instead of recurrent neural networks (RNN). The Multi-head attention is a crucial part that implements the self-attention mechanism, and it also dramatically affects the scale of the model. In this paper, we present a new Multi-head attention by combining convolution operation. In comparison with the base Transformer, our approach can reduce the number of parameters effectively. And we perform a reasoned experiment. The result shows that the performance of the new model is similar to the base model.