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arXiv · 2012.05786

Exploring Pair-Wise NMT for Indian Languages

Kartheek Akella·Sai Himal Allu·Sridhar Suresh Ragupathi·Aman Singhal·Zeeshan Khan·Vinay P. Namboodiri·C V Jawahar
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Abstract

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.

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Kartheek Akella, Sai Himal Allu, Sridhar Suresh Ragupathi, Aman Singhal, Zeeshan Khan, Vinay P. Namboodiri, C V Jawahar. 2020-12-10. Exploring Pair-Wise NMT for Indian Languages. https://arxiv.org/abs/2012.05786

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