International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 6 November-December 2024 Submit your research before last 3 days of December to publish your research paper in the issue of November-December.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 6 Issue 6
November-December 2024

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Machine Translation with Neural Networks Based on a Transformer

Author(s) Chaali Kaoutar, Lachhab Yasser, Sefiani Maha
Country Morocco
Abstract Over the same period, thanks to deep neural networks, particularly in machine translation tasks that fall under natural language process (NLP), there have been major advances. Context is not something that lies well with traditional systems based on statistical models or tranlation memory and they also struggle with the level of fluency in translation. Through the invention of their Transformer models, Vaswani et al. This has also made great strides in improving both precision and performance [28] from 2017.
In this paper, we explore how to adapt pre-trained Transformer models into low-resource languages. For lower-engaged languages, we want to introduce hybrid methods in machine translation systems using multi-task learning and also transfer-learning with different metrics. To address this, we exploit the high-resource languages for training and introduce representation reduction techniques to be able to better handle some low-resourced cases.
Our results show that multi-task training improves BLEU scores by a large margin, especially for more scarce languages such as Swahili and Amharic. Our results exemplify the power of combining effective multi-task training with transfer learning in low-resource language translation.
Keywords Natural Language Processing (NLP), Machine Translation , Transformer Models ,Low-Resource Languages ,Multi-Task Learning , Transfer Learning, Vocabulary Reduction , Neural Networks , BLEU Score , Language Models .
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 5, September-October 2024
Published On 2024-09-04
Cite This Machine Translation with Neural Networks Based on a Transformer - Chaali Kaoutar, Lachhab Yasser, Sefiani Maha - IJFMR Volume 6, Issue 5, September-October 2024. DOI 10.36948/ijfmr.2024.v06i05.26674
DOI https://doi.org/10.36948/ijfmr.2024.v06i05.26674
Short DOI https://doi.org/gt9kvb
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 6Cover Page Vol 6 Isu 5Cover Page Vol 6 Isu 4

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9898-5948-55 editor@ijfmr.com