Neural Machine Translation between Myanmar and Korean Languages

Abstract

No matter where they are in the world, people or individuals may now easily and cheaply communicate with one another because of the Internet. In addition to maintaining social networks and relationships, people can discover and exchange ideas. In addition, Natural Language Processing (NLP), a branch of Artificial Intelligence and Linguistics, investigates issues related to the automated generation and comprehension of natural human languages. It also aims to provide computers with the ability to comprehend instructions given to them in commonly used human languages. And then, NLP is an effort to allow users to communicate with computer in a natural language. However, communication with people from other countries continues to be restricted by language barriers, which are still a significant barrier. Consequently, there are increasingly more difficulties in translating from one language to another. The researchers are investigating machine translation to help users quickly translate from one language to several languages in order to solve these challenges. As a result, machine translation is growing in popularity among researchers and helps to reduce linguistic barriers. Artificial intelligence (AI) is employed in machine translation to automatically translate text between languages without the assistance of human interpreters. Even though there have only been a few studies on machine translation systems for translating from Myanmar to another language, there are still some difficulties in the early stages due to a lack of resources and a small number of publicly available data corpus. The aim of this paper is to develop a neural machine translation system implementation for the languages of Myanmar and Korean. There iv are two primary sections to this performance. The creation of a new Myanmar-Korean parallel corpus is the early part. The attention-based neural machine translation system for the Myanmar-Korean language pair is introduced in the second. The baseline system for the proposed model's experiments is a word-based neural machine translation model. BLEU (Bilingual Evaluation Understudy) score is used to evaluate on the Myanmar-Korean translation results.