@inproceedings{sainik-etal-2023-transfer,
title = "Transfer learning in low-resourced {MT}: An empirical study",
author = "Mahata, Sainik Kumar and
Saha, Dipanjan and
Das, Dipankar and
Bandyopadhyay, Sivaji",
editor = "D. Pawar, Jyoti and
Lalitha Devi, Sobha",
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 Mahata, Sainik Kumar
%A Saha, Dipanjan
%A Das, Dipankar
%A Bandyopadhyay, Sivaji
%Y D. Pawar, Jyoti
%Y Lalitha Devi, Sobha
%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/) (Mahata et al., ICON 2023)
ACL
- Sainik Kumar Mahata, Dipanjan Saha, Dipankar Das, and Sivaji Bandyopadhyay. 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).