A Linguistics-based deep learning Approach to ETL for Automated Translation of English Language Data

Abstract

Automated translation of information regarding English language has become imperative in this worldwide phenomenon. The erstwhile methods of performing Extract, Transform, and Load (ETL) of data involve a lot of manual effort besides taking a long time and being error-prone. A linguistics-based deep learning approach is proposed to improve the efficiency and accuracy of ETL for automated translation of English language data. The particular approach is focused on the adoption of deep learning techniques for automatically processing and translating data in English language without extensive manual intervention. In conjunction with this, it provides the use of linguistics knowledge, such as syntax, semantics, and grammar, for building an accurate deep learning model for the extract-transform-load process of being applied in the translation process. The method has also proved to be promising in experimentation with results competing favorably with common ETL methods speed and accuracy and showing good scalability. Linguistic insights in combination with deep learning have created for the specified approach the possibility of bringing efficiency, accuracy, and automation to the translation of the English language.