@inproceedings{sainik-etal-2023-transfer,
title = "Transfer learning in low-resourced {MT}: An empirical study",
author = "Sainik, Mahata and
Dipanjan, Saha and
Dipankar, Das and
Sivaji, Bandyopadhyay",
editor = "Jyoti, D. Pawar and
Sobha, Lalitha Devi",
booktitle = "Proceedings of the 20th International Conference on Natural Language Processing (ICON)",
month = dec,
year = "2023",
address = "Goa University, Goa, India",
publisher = "NLP Association of India (NLPAI)",
url = "https://aclanthology.org/2023.icon-1.63",
pages = "646--650",
abstract = "Translation systems rely on a large and goodquality parallel corpus for producing reliable translations. However, obtaining such a corpus for low-resourced languages is a challenge. New research has shown that transfer learning can mitigate this issue by augmenting lowresourced MT systems with high-resourced ones. In this work, we explore two types of transfer learning techniques, namely, crosslingual transfer learning and multilingual training, both with information augmentation, to examine the degree of performance improvement following the augmentation. Furthermore, we use languages of the same family (Romanic, in our case), to investigate the role of the shared linguistic property, in producing dependable translations.",
}
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<abstract>Translation systems rely on a large and goodquality parallel corpus for producing reliable translations. However, obtaining such a corpus for low-resourced languages is a challenge. New research has shown that transfer learning can mitigate this issue by augmenting lowresourced MT systems with high-resourced ones. In this work, we explore two types of transfer learning techniques, namely, crosslingual transfer learning and multilingual training, both with information augmentation, to examine the degree of performance improvement following the augmentation. Furthermore, we use languages of the same family (Romanic, in our case), to investigate the role of the shared linguistic property, in producing dependable translations.</abstract>
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%0 Conference Proceedings
%T Transfer learning in low-resourced MT: An empirical study
%A Sainik, Mahata
%A Dipanjan, Saha
%A Dipankar, Das
%A Sivaji, Bandyopadhyay
%Y Jyoti, D. Pawar
%Y Sobha, Lalitha Devi
%S Proceedings of the 20th International Conference on Natural Language Processing (ICON)
%D 2023
%8 December
%I NLP Association of India (NLPAI)
%C Goa University, Goa, India
%F sainik-etal-2023-transfer
%X Translation systems rely on a large and goodquality parallel corpus for producing reliable translations. However, obtaining such a corpus for low-resourced languages is a challenge. New research has shown that transfer learning can mitigate this issue by augmenting lowresourced MT systems with high-resourced ones. In this work, we explore two types of transfer learning techniques, namely, crosslingual transfer learning and multilingual training, both with information augmentation, to examine the degree of performance improvement following the augmentation. Furthermore, we use languages of the same family (Romanic, in our case), to investigate the role of the shared linguistic property, in producing dependable translations.
%U https://aclanthology.org/2023.icon-1.63
%P 646-650
Markdown (Informal)
[Transfer learning in low-resourced MT: An empirical study](https://aclanthology.org/2023.icon-1.63) (Sainik et al., ICON 2023)
ACL
- Mahata Sainik, Saha Dipanjan, Das Dipankar, and Bandyopadhyay Sivaji. 2023. Transfer learning in low-resourced MT: An empirical study. In Proceedings of the 20th International Conference on Natural Language Processing (ICON), pages 646–650, Goa University, Goa, India. NLP Association of India (NLPAI).