A Comparative Study on Transformer Versus Sequence to Sequence in Machine Translation

Abstract

Machine translation is the process of translating one natural language into another natural language. In the experiment, machine translation tasks were performed on the English to German data set and the English to Thai data set through the sequence-to-sequence model, the sequence-to-sequence model with attention mechanism and the transformer model. Through the analysis of the experimental data, it is concluded that the transformer model is not only better than the first two models in the performance of machine translation, but also the structural characteristics of the transformer model. When the data set is a relatively rare English to Thai data set, the transformer model is collected, and the impact of the result is less than the first two, which proves that the transformer model improves the quality of machine translation.