Context-Aware Neural Translation Framework: Enhancing Multilingual Accuracy and Real-World Adaptability through Optimized Deep NLP Models

Jaisharma K, Baiju Krishnan, Sumathi B, P. Chellammal, Vaithiyanathan R, A Nagamani

2025

Abstract

Given the pressing need for accurate and real-time multi-lingual communication, in this paper, we introduce a context-aware neural translation framework aimed at raising the quality of MT across varying languages and domains. Through the introduction of transformer-based architectures, domain-adaptive fine-tuning, and semantic alignment mechanisms, the model mitigates issues with low-resource language performance and semantic distortion, as well as zero-shot translation inconsistency. It incorporates hybrid evaluation techniques and works with real-world data-sets to develop its robustness, adaptability and linguistic fidelity. Furthermore, model optimization strategies are implemented to trade-off between computational efficiency and output quality. The proposed approach not only solves crosslanguage gap problem across the world, but also outperforms state-of-the-art NMT system.

Paper Citation

in Harvard Style

K J., Krishnan B., B S., Chellammal P., R V. and Nagamani A. (2025). Context-Aware Neural Translation Framework: Enhancing Multilingual Accuracy and Real-World Adaptability through Optimized Deep NLP Models. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 137-144. DOI: 10.5220/0013859100004919

in Bibtex Style

@conference{icrdicct`2525,
author={Jaisharma K and Baiju Krishnan and Sumathi B and P. Chellammal and Vaithiyanathan R and A Nagamani},
title={Context-Aware Neural Translation Framework: Enhancing Multilingual Accuracy and Real-World Adaptability through Optimized Deep NLP Models},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25},
year={2025},
pages={137-144},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013859100004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - Volume 1: ICRDICCT`25
TI - Context-Aware Neural Translation Framework: Enhancing Multilingual Accuracy and Real-World Adaptability through Optimized Deep NLP Models
SN - 978-989-758-777-1
AU - K J.
AU - Krishnan B.
AU - B S.
AU - Chellammal P.
AU - R V.
AU - Nagamani A.
PY - 2025
SP - 137
EP - 144
DO - 10.5220/0013859100004919
PB - SciTePress