@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{\textrightarrow}Marathi and English{\ensuremath{\Leftrightarrow}}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{\textrightarrow}Marathi, using external parallel corpus as input for additional training. We have used a pretrained Helsinki-NLP Opus MT English{\ensuremath{\Leftrightarrow}}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{\textrightarrow}Marathi, Irish{\textrightarrow}English, and English{\textrightarrow}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\ensuremathŁeftrightarrowIrish 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\ensuremathŁeftrightarrowIrish 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\ensuremathŁeftrightarrowIrish 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\ensuremathŁeftrightarrowIrish 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