Revolutionizing Multilingual Translation - A Novel Approach using Hybrid NMTs

Abstract

The demand for multilingual communication, particularly in linguistically diverse regions like India, drives the need for advanced machine translation (MT) systems. This paper presents a hybrid Neural Machine Translation (NMT) framework, integrating T5, ELECTRA, Big-Bird, GPT, and Gemma, fine-tuned on the ‘PMIndia’ dataset for 13 Indian languages using adapter techniques. Optimized with quantization, pruning, and serverless computing, the system aims to enhance translation quality and efficiency. We explore the theoretical evolution of MT, detail the adapter-based methodology, and outline a two-layer architecture. Experimental results, evaluated on an NVIDIA cloud environment and deployed via ‘ngrok’, show strong BLEU score improvements for 9 languages, with challenges for 4, analyzed using comparative plots. Future directions are proposed to refine this inclusive, scalable framework.

Introduction

India’s immense linguistic diversity—with over 1,600 languages and 22 official ones—creates a strong need for effective machine translation (MT). The PMIndia dataset, featuring parallel corpora for 13 major Indian languages, offers a valuable resource but also presents challenges such as high computational costs, data scarcity, and linguistic complexity.

This research proposes a hybrid Neural Machine Translation (NMT) system that combines five advanced models—T5, ELECTRA, Big-Bird, GPT, and Gemma—fine-tuned efficiently using adapter modules. The system leverages quantization, pruning, and serverless cloud deployment to deliver scalable, high-quality translations in real time.

The study reviews MT evolution:

• Rule-Based MT (RBMT) emphasized precision but was rigid and costly.

• Statistical MT (SMT) improved fluency with probabilistic models but struggled with limited data and context.

• Neural MT (NMT) uses deep learning and Transformers to capture complex context but requires heavy computation and large datasets.

Adapters enable lightweight fine-tuning by updating only a small fraction of parameters, making the hybrid system adaptable to multilingual Indian contexts with lower resource demands.

The hybrid model’s architecture cascades these components: T5 encodes text, ELECTRA refines token features, Big-Bird handles long-range context, GPT generates fluent output, and Gemma provides efficient computation. A fusion layer integrates their outputs.

Deployed on a serverless cloud platform, the system achieves significant reductions in latency and memory use while maintaining translation quality. The front-end GUI supports user-friendly, real-time translation across devices.

Conclusion

This research presents a hybrid NMT framework that advances multilingual communication by integrating T5, ELECTRA, Big-Bird, GPT, and Gemma, optimized through quantization, pruning, and serverless computing. Expanded across a two-layer architecture, it achieves exceptional translation quality and efficiency, addressing computational and inclusivity challenges in prior MT systems. Limitations include training complexity and potential serverless latency in edge cases, particularly for very-low-resource languages with minimal data. Future work could explore dynamic weighting of model components, few-shot learning for scarce datasets, edge computing to minimize latency, and user-driven refinement via feedback loops. This framework represents a milestone in MT evolution, offering a scalable, inclusive solution with significant potential for further enhancement.

References

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Copyright

Copyright © 2025 Dr. Sneha Khaire, Deep Chabhaiya, Chetan Killewale, Advait Khandalkar, Divya Ahire. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.