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

被引：0
|
作者：
Koehn, Philipp [1 ]
Guzman, Francisco [2 ]
Chaudhary, Vishrav [2 ]
Pino, Juan [2 ]
机构：
[1] Johns Hopkins Univ, Baltimore, MD 21218 USA
[2] Facebook AI, Menlo Pk, CA USA
来源：
FOURTH CONFERENCE ON MACHINE TRANSLATION (WMT 2019), VOL 3: SHARED TASK PAPERS, DAY 2 | 2019年
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D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Following the WMT 2018 Shared Task on Parallel Corpus Filtering (Koehn et al., 2018), 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.
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页码：54 / 72
页数：19
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