Abstract
In this article, we describe the TALP-UPC participation in the WMT20 news translation shared task for Tamil-English. Given the low amount of parallel training data, we resort to adapt the task to a multilingual system to benefit from the positive transfer from high resource languages. We use iterative backtranslation to fine-tune the system and benefit from the monolingual data available. In order to measure the effectivity of such methods, we compare our results to a bilingual baseline system.- Anthology ID:
- 2020.wmt-1.10
- Volume:
- Proceedings of the Fifth Conference on Machine Translation
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Yvette Graham, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, André Martins, Makoto Morishita, Christof Monz, Masaaki Nagata, Toshiaki Nakazawa, Matteo Negri
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 134–138
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.10
- DOI:
- Cite (ACL):
- Carlos Escolano, Marta R. Costa-jussà, and José A. R. Fonollosa. 2020. The TALP-UPC System Description for WMT20 News Translation Task: Multilingual Adaptation for Low Resource MT. In Proceedings of the Fifth Conference on Machine Translation, pages 134–138, Online. Association for Computational Linguistics.
- Cite (Informal):
- The TALP-UPC System Description for WMT20 News Translation Task: Multilingual Adaptation for Low Resource MT (Escolano et al., WMT 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.wmt-1.10.pdf