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Attentive fine-tuning of Transformers for Translation of low-resourced languages @LoResMT 2021

2021-08-19 08:46:14
Karthik Puranik, Adeep Hande, Ruba Priyadharshini, Thenmozi Durairaj, Anbukkarasi Sampath, Kingston Pal Thamburaj, Bharathi Raja Chakravarthi
arXiv_CL
     
Abstract

This paper reports the Machine Translation (MT) systems submitted by the IIITT team for the English->Marathi and English->Irish language pairs LoResMT 2021 shared task. The task focuses on getting exceptional translations for rather low-resourced languages like Irish and Marathi. We fine-tune IndicTrans, a pretrained multilingual NMT model for English->Marathi, using external parallel corpus as input for additional training. We have used a pretrained Helsinki-NLP Opus MT English->Irish model for the latter language pair. Our approaches yield relatively promising results on the BLEU metrics. Under the team name IIITT, our systems ranked 1, 1, and 2 in English->Marathi, Irish->English, and English->Irish, respectively.

Abstract (translated)

URL

https://arxiv.org/abs/2108.08556

PDF

https://arxiv.org/pdf/2108.08556.pdf

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