Machine Translation in Natural Language Processing by Implementing Artificial Neural Network Modelling Techniques: An Analysis

Authors

  • Fazeel Ahmed Khan International Islamic University Malaysia
  • Adamu Abubakar

Abstract

Natural Language Processing is emerging with more efficient algorithms to perform detailed analysis and synthesis on different languages and speech translation with techniques from computer science. Machine translation is emerging from Statistical Machine Translation to a more efficient and robust oriented deep learning-based Neural Machine Translation. The limitation in Statistical based MT opens a new spectrum of research in NMT to resolve the existing problems and explore NMT potential in MT research. This paper comprehensively analyses various NMT models proposed in recent years and their contribution in resolving language translation issues. It also discusses on some NMT based open-source toolkits introduced in recent years and the feature implemented in these toolkits. It also analyses the potential of these toolkits to comply with research in language translation particularly in NMT based techniques.

Downloads

Published

2020-07-02 — Updated on 2020-07-02

Versions

How to Cite

Khan, F. A., & Abubakar, A. (2020). Machine Translation in Natural Language Processing by Implementing Artificial Neural Network Modelling Techniques: An Analysis. International Journal on Perceptive and Cognitive Computing, 6(1), 9–18. Retrieved from https://journals.iium.edu.my/kict/index.php/IJPCC/article/view/134

Issue

Section

Articles