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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Machine Translation System Using Deep Learning for English to Urdu.

Syed Abdul Basit Andrabi¹, Abdul Wahid¹

¹Department of Computer Science and Information Technology, Maulana Azad National Urdu University, Hyderabad, Telangana 500032, India.

Computational Intelligence and Neuroscience

|January 13, 2022

Summary

This study introduces a deep learning model for English to Urdu machine translation, achieving a high BLEU score. The research aims to break down language barriers using advanced neural network techniques.

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Area of Science:

Computational Linguistics
Artificial Intelligence

Background:

Machine translation (MT) has evolved significantly from word-to-word systems to sophisticated data-driven models.
Advancements in computing have enabled the development of statistical and neural machine translation (NMT).

Purpose of the Study:

To develop and evaluate a neural network-based deep learning model for English to Urdu machine translation.
To assess the effectiveness of the proposed NMT system using automatic evaluation metrics and comparison with existing tools.

Main Methods:

Utilized a neural network-based deep learning technique for English to Urdu translation.
Trained and tested the model on a parallel corpus of 30,923 sentences (English-Urdu, news, daily life).
Employed a 70:30 training-testing split and automatic evaluation metrics, including BLEU score.

Main Results:

The proposed deep learning model achieved an average BLEU score of 45.83.
Performance was benchmarked against Google Translator's output.
The system demonstrated proficiency in translating common English sentences to Urdu.

Conclusions:

Deep learning techniques show significant promise for improving English to Urdu machine translation.
The developed model offers a viable solution for reducing language barriers between English and Urdu speakers.
Further research can explore larger datasets and advanced NMT architectures.