  Transformer is a state-of-the-art model in the field of natural language
processing (NLP). Current NLP models primarily increase the number of
transformers to improve processing performance. However, this technique
requires a lot of training resources such as computing capacity. In this paper,
a novel structure of Transformer is proposed. It is featured by full layer
normalization, weighted residual connection, positional encoding exploiting
reinforcement learning, and zero masked self-attention. The proposed
Transformer model, which is called Enhanced Transformer, is validated by the
bilingual evaluation understudy (BLEU) score obtained with the Multi30k
translation dataset. As a result, the Enhanced Transformer achieves 202.96%
higher BLEU score as compared to the original transformer with the translation
dataset.

Computation and Language;Artificial Intelligence