Findings of the WMT 2019 Shared Task on Parallel Corpus Filtering for Low-Resource Conditions

Abstract

Following the WMT 2018 Shared Task on Parallel Corpus Filtering (Koehn et al., 2018), we posed the challenge of assigning sentencelevel 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.