Papers › Attentive fine-tuning of Transformers for Translation of low-resourced languages @LoResMT 2021
Attentive fine-tuning of Transformers for Translation of low-resourced languages @LoResMT 2021
Karthik Puranik, Adeep Hande, Ruba Priyadharshini, Thenmozhi Durairaj, Anbukkarasi Sampath, Kingston Pal Thamburaj, Bharathi Raja Chakravarthi
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.
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