High-Resource Translation:Turning Abundance into Accessibility

Abstract

This paper presents a novel approach to constructing an English-to-Telugu translation model by leveraging transfer learning techniques and addressing the challenges associated with low-resource languages. Utilizing the Bharat Parallel Corpus Collection (BPCC) as the primary dataset, the model incorporates iterative backtranslation to generate synthetic parallel data, effectively augmenting the training dataset and enhancing the model's translation capabilities. The research focuses on a comprehensive strategy for improving model performance through data augmentation, optimization of training parameters, and the effective use of pre-trained models. These methodologies aim to create a robust translation system that can handle diverse sentence structures and linguistic nuances in both English and Telugu. This work highlights the significance of innovative data handling techniques and the potential of transfer learning in overcoming limitations posed by sparse datasets in low-resource languages. The study contributes to the field of machine translation and seeks to improve communication between English and Telugu speakers in practical contexts.