@inproceedings{puranik-etal-2021-attentive,
title = "Attentive fine-tuning of Transformers for Translation of low-resourced languages @{L}o{R}es{MT} 2021",
author = "Puranik, Karthik and
Hande, Adeep and
Priyadharshini, Ruba and
Durairaj, Thenmozi and
Sampath, Anbukkarasi and
Pal Thamburaj, Kingston and
Chakravarthi, Bharathi Raja",
editor = "Ortega, John and
Ojha, Atul Kr. and
Kann, Katharina and
Liu, Chao-Hong",
booktitle = "Proceedings of the 4th Workshop on Technologies for MT of Low Resource Languages (LoResMT2021)",
month = aug,
year = "2021",
address = "Virtual",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2021.mtsummit-loresmt.14",
pages = "134--143",
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. The codes for our systems are published1 .",
}
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<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. The codes for our systems are published1 .</abstract>
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%0 Conference Proceedings
%T Attentive fine-tuning of Transformers for Translation of low-resourced languages @LoResMT 2021
%A Puranik, Karthik
%A Hande, Adeep
%A Priyadharshini, Ruba
%A Durairaj, Thenmozi
%A Sampath, Anbukkarasi
%A Pal Thamburaj, Kingston
%A Chakravarthi, Bharathi Raja
%Y Ortega, John
%Y Ojha, Atul Kr.
%Y Kann, Katharina
%Y Liu, Chao-Hong
%S Proceedings of the 4th Workshop on Technologies for MT of Low Resource Languages (LoResMT2021)
%D 2021
%8 August
%I Association for Machine Translation in the Americas
%C Virtual
%F puranik-etal-2021-attentive
%X 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. The codes for our systems are published1 .
%U https://aclanthology.org/2021.mtsummit-loresmt.14
%P 134-143
Markdown (Informal)
[Attentive fine-tuning of Transformers for Translation of low-resourced languages @LoResMT 2021](https://aclanthology.org/2021.mtsummit-loresmt.14) (Puranik et al., LoResMT 2021)
ACL