Research on Key Technologies of Neural Machine Translation Based on Deep Learning

Kaijie Lai

2024

Abstract

Machine Learning is a technology aiming to finish automatic translation from one language to another by using a computer. Such a technique plays a significant role in promoting cultural communication and boosting the economic development. Nowadays, the fast development and enormous utilization of Deep Learning and Neural Networks in Natural Language Processing (NLP) field generate a new concept called Neural Machine Translation (NMT). The way to integrate Neural Networks into NMT models has a promising future. And after several years’ development, progress in NMT has been made on all fronts. Learning different Deep Learning based Machine Translation models is meaningful to acquire a better understanding about NMT. To show the research progress, based on the proposed time, the article discussed some famous NMT models through their operating principle, innovation points and problems remained, highlighting the progression from Recurrent Neural Network (RNN) Encoder-Decoder architecture in NMT to the powerful Transformer model. The article also sorts out and compares the performance of the NMT models mentioned based on their performances on some public corpuses. Finally, the paper discusses the future directions in NMT, such as solving challenges for low-resource languages’ translation, developing multilingual NMT models and increasing the models’ interpretability.

Paper Citation

in Harvard Style

Lai K. (2024). Research on Key Technologies of Neural Machine Translation Based on Deep Learning. In Proceedings of the 1st International Conference on Modern Logistics and Supply Chain Management - Volume 1: MLSCM; ISBN 978-989-758-738-2, SciTePress, pages 60-65. DOI: 10.5220/0013231400004558

in Bibtex Style

@conference{mlscm24,
author={Kaijie Lai},
title={Research on Key Technologies of Neural Machine Translation Based on Deep Learning},
booktitle={Proceedings of the 1st International Conference on Modern Logistics and Supply Chain Management - Volume 1: MLSCM},
year={2024},
pages={60-65},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013231400004558},
isbn={978-989-758-738-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Modern Logistics and Supply Chain Management - Volume 1: MLSCM
TI - Research on Key Technologies of Neural Machine Translation Based on Deep Learning
SN - 978-989-758-738-2
AU - Lai K.
PY - 2024
SP - 60
EP - 65
DO - 10.5220/0013231400004558
PB - SciTePress