Abstract
This paper reports a Machine Translation (MT) system submitted by the NLPRL team for the Bhojpuri–Hindi and Magahi–Hindi language pairs at LoResMT 2020 shared task. We used an unsupervised domain adaptation approach that gives promising results for zero or extremely low resource languages. Task organizers provide the development and the test sets for evaluation and the monolingual data for training. Our approach is a hybrid approach of domain adaptation and back-translation. Metrics used to evaluate the trained model are BLEU, RIBES, Precision, Recall and F-measure. Our approach gives relatively promising results, with a wide range, of 19.5, 13.71, 2.54, and 3.16 BLEU points for Bhojpuri to Hindi, Magahi to Hindi, Hindi to Bhojpuri and Hindi to Magahi language pairs, respectively.- Anthology ID:
- 2020.loresmt-1.6
- Volume:
- Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages
- Month:
- December
- Year:
- 2020
- Address:
- Suzhou, China
- Editors:
- Alina Karakanta, Atul Kr. Ojha, Chao-Hong Liu, Jade Abbott, John Ortega, Jonathan Washington, Nathaniel Oco, Surafel Melaku Lakew, Tommi A Pirinen, Valentin Malykh, Varvara Logacheva, Xiaobing Zhao
- Venue:
- LoResMT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 43–46
- Language:
- URL:
- https://aclanthology.org/2020.loresmt-1.6
- DOI:
- Cite (ACL):
- Amit Kumar, Rajesh Kumar Mundotiya, and Anil Kumar Singh. 2020. Unsupervised Approach for Zero-Shot Experiments: Bhojpuri–Hindi and Magahi–Hindi@LoResMT 2020. In Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages, pages 43–46, Suzhou, China. Association for Computational Linguistics.
- Cite (Informal):
- Unsupervised Approach for Zero-Shot Experiments: Bhojpuri–Hindi and Magahi–Hindi@LoResMT 2020 (Kumar et al., LoResMT 2020)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/2020.loresmt-1.6.pdf