The TALP-UPC Machine Translation Systems for WMT19 News Translation Task: Pivoting Techniques for Low Resource MT
Noe Casas, José A. R. Fonollosa, Carlos Escolano, Christine Basta, Marta R. Costa-jussà
Abstract
In this article, we describe the TALP-UPC research group participation in the WMT19 news translation shared task for Kazakh-English. Given the low amount of parallel training data, we resort to using Russian as pivot language, training subword-based statistical translation systems for Russian-Kazakh and Russian-English that were then used to create two synthetic pseudo-parallel corpora for Kazakh-English and English-Kazakh respectively. Finally, a self-attention model based on the decoder part of the Transformer architecture was trained on the two pseudo-parallel corpora.- Anthology ID:
- W19-5311
- 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:
- 155–162
- Language:
- URL:
- https://aclanthology.org/W19-5311
- DOI:
- 10.18653/v1/W19-5311
- Cite (ACL):
- Noe Casas, José A. R. Fonollosa, Carlos Escolano, Christine Basta, and Marta R. Costa-jussà. 2019. The TALP-UPC Machine Translation Systems for WMT19 News Translation Task: Pivoting Techniques for Low Resource MT. In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), pages 155–162, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- The TALP-UPC Machine Translation Systems for WMT19 News Translation Task: Pivoting Techniques for Low Resource MT (Casas et al., WMT 2019)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/W19-5311.pdf
- Data
- United Nations Parallel Corpus