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
- 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/auto-file-uploads/W19-5311.pdf
- Data
- United Nations Parallel Corpus