In this paper, we address the task of improving pair-wise machine translation for specific low resource Indian languages. Multilingual NMT models have demonstrated a reasonable amount of effectiveness on resource-poor languages. In this work, we show that the performance of these models can be significantly improved upon by using back-translation through a filtered back-translation process and subsequent fine-tuning on the limited pair-wise language corpora. The analysis in this paper suggests that this method can significantly improve a multilingual model's performance over its baseline, yielding state-of-the-art results for various Indian languages.
@article{arxiv.2012.05786,
title = {Exploring Pair-Wise NMT for Indian Languages},
author = {Kartheek Akella and Sai Himal Allu and Sridhar Suresh Ragupathi and Aman Singhal and Zeeshan Khan and Vinay P. Namboodiri and C V Jawahar},
journal= {arXiv preprint arXiv:2012.05786},
year = {2020}
}