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
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:
54–72
Language:
URL:
https://aclanthology.org/W19-5404
DOI:
10.18653/v1/W19-5404
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/W19-5404.pdf