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        <title>On the Transferability of Massively Multilingual Pretrained Models in the Pretext of the Indo-Aryan and Tibeto-Burman Languages</title>
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        <namePart type="given">Loitongbam</namePart>
        <namePart type="family">Sanayai Meetei</namePart>
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        <namePart type="given">Sivaji</namePart>
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            <title>Proceedings of the 18th International Conference on Natural Language Processing (ICON)</title>
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            <namePart type="given">Sobha</namePart>
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            <namePart type="family">Devi</namePart>
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    <abstract>In recent times, machine translation models can learn to perform implicit bridging between language pairs never seen explicitly during training and showing that transfer learning helps for languages with constrained resources. This work investigates the low resource machine translation via transfer learning from multilingual pre-trained models i.e. mBART-50 and mT5-base in the pretext of Indo-Aryan (Assamese and Bengali) and Tibeto-Burman (Manipuri) languages via finetuning as a downstream task. Assamese and Manipuri were absent in the pretraining of both mBART-50 and the mT5 models. However, the experimental results attest that the finetuning from these pre-trained models surpasses the multilingual model trained from scratch.</abstract>
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%0 Conference Proceedings
%T On the Transferability of Massively Multilingual Pretrained Models in the Pretext of the Indo-Aryan and Tibeto-Burman Languages
%A Singh, Salam Michael
%A Sanayai Meetei, Loitongbam
%A Singh, Alok
%A Singh, Thoudam Doren
%A Bandyopadhyay, Sivaji
%Y Bandyopadhyay, Sivaji
%Y Devi, Sobha Lalitha
%Y Bhattacharyya, Pushpak
%S Proceedings of the 18th International Conference on Natural Language Processing (ICON)
%D 2021
%8 December
%I NLP Association of India (NLPAI)
%C National Institute of Technology Silchar, Silchar, India
%F singh-etal-2021-transferability
%X In recent times, machine translation models can learn to perform implicit bridging between language pairs never seen explicitly during training and showing that transfer learning helps for languages with constrained resources. This work investigates the low resource machine translation via transfer learning from multilingual pre-trained models i.e. mBART-50 and mT5-base in the pretext of Indo-Aryan (Assamese and Bengali) and Tibeto-Burman (Manipuri) languages via finetuning as a downstream task. Assamese and Manipuri were absent in the pretraining of both mBART-50 and the mT5 models. However, the experimental results attest that the finetuning from these pre-trained models surpasses the multilingual model trained from scratch.
%U https://aclanthology.org/2021.icon-main.9
%P 64-74

Markdown (Informal)

[On the Transferability of Massively Multilingual Pretrained Models in the Pretext of the Indo-Aryan and Tibeto-Burman Languages](https://aclanthology.org/2021.icon-main.9) (Singh et al., ICON 2021)

• On the Transferability of Massively Multilingual Pretrained Models in the Pretext of the Indo-Aryan and Tibeto-Burman Languages (Singh et al., ICON 2021)

ACL

• Salam Michael Singh, Loitongbam Sanayai Meetei, Alok Singh, Thoudam Doren Singh, and Sivaji Bandyopadhyay. 2021. On the Transferability of Massively Multilingual Pretrained Models in the Pretext of the Indo-Aryan and Tibeto-Burman Languages. In Proceedings of the 18th International Conference on Natural Language Processing (ICON), pages 64–74, National Institute of Technology Silchar, Silchar, India. NLP Association of India (NLPAI).