Bridging the Dravidian Language Gap: A Back Translation Enhanced Transformer Framework for Brahui‐English Neural Machine Translation

ABSTRACT

The presented paper introduces a Brahui‐to‐English NMT system developed for a severely low‐resource scenario. The framework adopted for creating the NMT system is based on the Marian MT Transformer model and augmented with back‐translation. In order to develop the model, a Brahui‐to‐English parallel corpus was initially cleaned up and divided into training, validation, and test sets. After this, the pipeline generated baseline translation models in both directions. Subsequently, the English‐to‐Brahui model was utilized to generate synthetic Brahui sentences on the basis of monolingual English data while fine‐tuning the original forward translation model. In total, the experiment was executed within Google Colab TP4, and the computation was run on a CUDA‐compatible computer for up to 16 h. The experimental settings include a max length of 96 for the sequences, 8 for the batch size, a gradient accumulation of 2, the learning rate of 3e−5, and three training epochs for both baselines and augmented models. During the evaluation of the results, such measures as BLEU, ChrF, and METEOR were applied, and the obtained scores indicate a clear evolutionary superiority. The framework progressed from a traditional Bi‐LSTM baseline 45.89 BLEU, 50.68 ChrF, 56.93 METEOR, to a standard Transformer baseline 48.43 BLEU, 57.29 ChrF, 62.08 METEOR, and culminated in the proposed BT‐augmented model, which achieved peak performance 53.87 BLEU, 61.85 ChrF, 65.91 METEOR. While standard NMT models frequently struggle to surpass BLEU scores of 15–40 for severely low‐resource and morphologically complex languages, the proposed framework achieves a highly competitive BLEU score of 53.87, setting a robust new benchmark for the Natural language processing field. The results presented above were obtained by conducting single‐point estimation runs, while multiseed training and statistical significance testing have been recognized as crucial tasks for future work.