Findings of the WMT 2019 Shared Task on Parallel Corpus Filtering for Low-Resource Conditions
Philipp Koehn, Francisco Guzmán, Vishrav Chaudhary, Juan Pino
Abstract
Following the WMT 2018 Shared Task on Parallel Corpus Filtering, we posed the challenge of assigning sentence-level quality scores for very noisy corpora of sentence pairs crawled from the web, with the goal of sub-selecting 2% and 10% of the highest-quality data to be used to train machine translation systems. This year, the task tackled the low resource condition of Nepali-English and Sinhala-English. Eleven participants from companies, national research labs, and universities participated in this task.- Anthology ID:
- W19-5404
- Volume:
- Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 54–72
- Language:
- URL:
- https://aclanthology.org/W19-5404
- DOI:
- 10.18653/v1/W19-5404
- Cite (ACL):
- Philipp Koehn, Francisco Guzmán, Vishrav Chaudhary, and Juan Pino. 2019. Findings of the WMT 2019 Shared Task on Parallel Corpus Filtering for Low-Resource Conditions. In Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pages 54–72, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Findings of the WMT 2019 Shared Task on Parallel Corpus Filtering for Low-Resource Conditions (Koehn et al., WMT 2019)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/W19-5404.pdf