Efficient Use of Machine Learning Models to Evaluate the Parametric Performance of the ML Models for Language Translation from Telugu to Hindi

A. Surya Kausthub, Yerukola Gayatri, Shail Garg, Peeta Basa Pati, Tania Ganguly

2025

Abstract

Translation between Telugu and Hindi, two widely spoken languages in India, presents numerous challenges due to significant linguistic, syntactic, and cultural differences. This study focuses on leveraging advanced deep learning models to address these discrepancies and evaluate their performance in translating Telugu to Hindi effectively. The research considers models such as Long Short-Term Memory Networks (LSTM) and Fairseq emphasizing their parametric performance by fine-tuning under various settings. The core objective is to systematically assess these models, uncovering how they respond to parameter optimization and identifying the best methodologies for generating high-quality translations. By analyzing the results, this study aims to pave the way for the development of robust and efficient translation systems tailored to low-resource languages like Telugu. Such systems hold the potential to bridge linguistic gaps and foster more accessible communication across diverse Indian languages, contributing to broader cultural and digital inclusion.From the two models studied Fairseq is a better model with higher accuracy.

Paper Citation

in Harvard Style

Kausthub A., Gayatri Y., Garg S., Basa Pati P. and Ganguly T. (2025). Efficient Use of Machine Learning Models to Evaluate the Parametric Performance of the ML Models for Language Translation from Telugu to Hindi. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 712-720. DOI: 10.5220/0013642800004664

in Bibtex Style

@conference{incoft25,
author={A. Surya Kausthub and Yerukola Gayatri and Shail Garg and Peeta Basa Pati and Tania Ganguly},
title={Efficient Use of Machine Learning Models to Evaluate the Parametric Performance of the ML Models for Language Translation from Telugu to Hindi},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT},
year={2025},
pages={712-720},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013642800004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT
TI - Efficient Use of Machine Learning Models to Evaluate the Parametric Performance of the ML Models for Language Translation from Telugu to Hindi
SN - 978-989-758-763-4
AU - Kausthub A.
AU - Gayatri Y.
AU - Garg S.
AU - Basa Pati P.
AU - Ganguly T.
PY - 2025
SP - 712
EP - 720
DO - 10.5220/0013642800004664
PB - SciTePress