  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.
