Improving Neural Machine Translation for Sanskrit-English

Ravneet Punia, Aditya Sharma, Sarthak Pruthi, Minni Jain


Abstract
Sanskrit is one of the oldest languages of the Asian Subcontinent that fell out of common usage around 600 B.C. In this paper, we attempt to translate Sanskrit to English using Neural Machine Translation approaches based on Reinforcement Learning and Transfer learning that were never tried and tested on Sanskrit. Along with the paper, we also release monolingual Sanskrit and parallel aligned Sanskrit-English corpora for the research community. Our methodologies outperform the previous approaches applied to Sanskrit by various re- searchers and will further help the linguistic community to accelerate the costly and time consuming manual translation process.
Anthology ID:
2020.icon-main.30
Volume:
Proceedings of the 17th International Conference on Natural Language Processing (ICON)
Month:
December
Year:
2020
Address:
Indian Institute of Technology Patna, Patna, India
Editors:
Pushpak Bhattacharyya, Dipti Misra Sharma, Rajeev Sangal
Venue:
ICON
SIG:
Publisher:
NLP Association of India (NLPAI)
Note:
Pages:
234–238
Language:
URL:
https://aclanthology.org/2020.icon-main.30
DOI:
Bibkey:
Cite (ACL):
Ravneet Punia, Aditya Sharma, Sarthak Pruthi, and Minni Jain. 2020. Improving Neural Machine Translation for Sanskrit-English. In Proceedings of the 17th International Conference on Natural Language Processing (ICON), pages 234–238, Indian Institute of Technology Patna, Patna, India. NLP Association of India (NLPAI).
Cite (Informal):
Improving Neural Machine Translation for Sanskrit-English (Punia et al., ICON 2020)
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PDF:
https://aclanthology.org/2020.icon-main.30.pdf