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2018 EMNLP EMNLP 2018

Findings of the WMT 2018 Shared Task on Parallel Corpus Filtering

Abstract

AbstractWe posed the shared task of assigning sentence-level quality scores for a very noisy corpus of sentence pairs crawled from the web, with the goal of sub-selecting 1% and 10% of high-quality data to be used to train machine translation systems. Seventeen participants from companies, national research labs, and universities participated in this task.

🧭 Keyword Pioneer - parallel corpus filtering

Authors

Philipp Koehn, Huda Khayrallah, Kenneth Heafield, Mikel L. Forcada

Topics

Natural Language Processing > Applications > Machine Translation Natural Language Processing > Resources & Methods > Text Representation

Keywords

parallel corpus filtering sentence-level quality corpus quality quality scoring machine translation training

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