# Exploring Pair-Wise NMT for Indian Languages

arXiv: https://arxiv.org/abs/2012.05786 · HTML: https://mlsanity.com/paper/2012.05786/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2020.

## Built on by (most cited first, 1 of 1)

- [More Parameters? No Thanks!](https://mlsanity.com/paper/2107.09622/) (2021)

## Abstract (arXiv)

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.