Unsupervised Neural Machine Translation for English to Kannada Using Pre-Trained Language Model
Shailashree K Sheshadri, B Sai Bharath, Alapati Hari Naga Sree Chandana Sarvani, Potta Reddy Vijaya Bharathi Reddy, Deepa Gupta · 2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT) · 2022
With the advent of Neural Networks, Neural Machine Translation (NMT) has been a booming area of research in Natural Language Processing. Indic languages possess less significant parallel and monolingual corpus than English and other European languages. Even in Indic languages, many Indo-Aryan and Dravidian languages fall into low-resource languages, and techniques like Back Translation, Unsupervised Neural Machine Translation (UNMT), and Transfer Learning help achieve better translation accuracy. This paper uses the UNMT approach with a pre-trained Cross-Lingual Language Model (XLM) for the English to Kannada language pair. Our proposed methodology has achieved a 0.61 BLEU score in English to Kannada and a 0.32 BLEU score in Kannada to English translation.