Abstract
This paper describes the Neural Machine Translation system of IIIT-Hyderabad for the Gujarati→English news translation shared task of WMT19. Our system is basedon encoder-decoder framework with attention mechanism. We experimented with Multilingual Neural MT models. Our experiments show that Multilingual Neural Machine Translation leveraging parallel data from related language pairs helps in significant BLEU improvements upto 11.5, for low resource language pairs like Gujarati-English- Anthology ID:
- W19-5316
- Volume:
- Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, André Martins, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Marco Turchi, Karin Verspoor
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 191–195
- Language:
- URL:
- https://aclanthology.org/W19-5316
- DOI:
- 10.18653/v1/W19-5316
- Cite (ACL):
- Vikrant Goyal and Dipti Misra Sharma. 2019. The IIIT-H Gujarati-English Machine Translation System for WMT19. In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), pages 191–195, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- The IIIT-H Gujarati-English Machine Translation System for WMT19 (Goyal & Sharma, WMT 2019)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/W19-5316.pdf