Evaluating the Performance of Back-translation for Low Resource English-Marathi Language Pair: CFILT-IITBombay @ LoResMT 2021

Aditya Jain, Shivam Mhaskar, Pushpak Bhattacharyya


Abstract
In this paper, we discuss the details of the various Machine Translation (MT) systems that we have submitted for the English-Marathi LoResMT task. As a part of this task, we have submitted three different Neural Machine Translation (NMT) systems; a Baseline English-Marathi system, a Baseline Marathi-English system, and an English-Marathi system that is based on the back-translation technique. We explore the performance of these NMT systems between English and Marathi languages, which forms a low resource language pair due to unavailability of sufficient parallel data. We also explore the performance of the back-translation technique when the back-translated data is obtained from NMT systems that are trained on a very less amount of data. From our experiments, we observe that the back-translation technique can help improve the MT quality over the baseline for the English-Marathi language pair.
Anthology ID:
2021.mtsummit-loresmt.17
Volume:
Proceedings of the 4th Workshop on Technologies for MT of Low Resource Languages (LoResMT2021)
Month:
August
Year:
2021
Address:
Virtual
Editors:
John Ortega, Atul Kr. Ojha, Katharina Kann, Chao-Hong Liu
Venue:
LoResMT
SIG:
Publisher:
Association for Machine Translation in the Americas
Note:
Pages:
158–162
Language:
URL:
https://aclanthology.org/2021.mtsummit-loresmt.17
DOI:
Bibkey:
Cite (ACL):
Aditya Jain, Shivam Mhaskar, and Pushpak Bhattacharyya. 2021. Evaluating the Performance of Back-translation for Low Resource English-Marathi Language Pair: CFILT-IITBombay @ LoResMT 2021. In Proceedings of the 4th Workshop on Technologies for MT of Low Resource Languages (LoResMT2021), pages 158–162, Virtual. Association for Machine Translation in the Americas.
Cite (Informal):
Evaluating the Performance of Back-translation for Low Resource English-Marathi Language Pair: CFILT-IITBombay @ LoResMT 2021 (Jain et al., LoResMT 2021)
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PDF:
https://aclanthology.org/2021.mtsummit-loresmt.17.pdf