JoWUA

Abstract

Neural machine translation is a machine translation system that uses artificial neural networks to identify nonlinear relationships between bilingual sentence pairs. The language English-Kannada pair has met with less attention in the field of Neural Machine Translation (NMT), mostly due to lack of parallel corpora and the complexity of Kannada's linguistic structure. This research proposes a novel NMT model based on Gated Recurrent Units (GRU) and a Hybrid attention mechanism designed specifically for Kannada's morphological complexity and compared with existing models. By adding language-specific preprocessing techniques and employing data augmentation tactics, our model outperforms previous LSTM, GRU and Bi-LSTM-based algorithms in terms of BLEU and METEOR scores. The proposed model with hybrid attention might be a useful substitute in low-resource settings because transformers and other large models require more resources. The comparative study indicates that the proposed methodology improves the translation by 10% increase in Bilingual Evaluation Understudy (BLEU) and accuracy of 80% and achieved a Rouge Score of 0.9.