Neural Machine Translation for Tamil–Telugu Pair

Sahinur Rahman Laskar, Bishwaraj Paul, Prottay Kumar Adhikary, Partha Pakray, Sivaji Bandyopadhyay

Abstract
The neural machine translation approach has gained popularity in machine translation because of its context analysing ability and its handling of long-term dependency issues. We have participated in the WMT21 shared task of similar language translation on a Tamil-Telugu pair with the team name: CNLP-NITS. In this task, we utilized monolingual data via pre-train word embeddings in transformer model based neural machine translation to tackle the limitation of parallel corpus. Our model has achieved a bilingual evaluation understudy (BLEU) score of 4.05, rank-based intuitive bilingual evaluation score (RIBES) score of 24.80 and translation edit rate (TER) score of 97.24 for both Tamil-to-Telugu and Telugu-to-Tamil translations respectively.
Anthology ID:
2021.wmt-1.29
Volume:
Proceedings of the Sixth Conference on Machine Translation
Month:
November
Year:
2021
Address:
Online
Editors:
Loic Barrault, Ondrej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussa, Christian Federmann, Mark Fishel, Alexander Fraser, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Tom Kocmi, Andre Martins, Makoto Morishita, Christof Monz
Venue:
WMT
SIG:
SIGMT
Publisher:
Association for Computational Linguistics
Note:
Pages:
284–287
Language:
URL:
https://aclanthology.org/2021.wmt-1.29
DOI:
Bibkey:
Cite (ACL):
Sahinur Rahman Laskar, Bishwaraj Paul, Prottay Kumar Adhikary, Partha Pakray, and Sivaji Bandyopadhyay. 2021. Neural Machine Translation for Tamil–Telugu Pair. In Proceedings of the Sixth Conference on Machine Translation, pages 284–287, Online. Association for Computational Linguistics.
Cite (Informal):
Neural Machine Translation for Tamil–Telugu Pair (Laskar et al., WMT 2021)
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