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.
@article{arxiv.2310.10930,
title = {Enhanced Transformer Architecture for Natural Language Processing},
author = {Woohyeon Moon and Taeyoung Kim and Bumgeun Park and Dongsoo Har},
journal= {arXiv preprint arXiv:2310.10930},
year = {2023}
}