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2019 ACL ACL 2019

Quality and Coverage: The AFRL Submission to the WMT19 Parallel Corpus Filtering for Low-Resource Conditions Task

Abstract

AbstractThe WMT19 Parallel Corpus Filtering For Low-Resource Conditions Task aims to test various methods of filtering a noisy parallel corpora, to make them useful for training machine translation systems. This year the noisy corpora are the relatively low-resource language pairs of Nepali-English and Sinhala-English. This papers describes the Air Force Research Laboratory (AFRL) submissions, including preprocessing methods and scoring metrics. Numerical results indicate a benefit over baseline and the relative benefits of different options.

🌉 Interdisciplinary Bridge — Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — preprocessing method
🐣 Hot Topic Early Bird — low-resource language
🐝 Cross-Pollinator — Artificial Intelligence, Computer Vision, Data Science & Analytics, Deep Learning, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Speech & Audio

Authors

Grant Erdmann , Jeremy Gwinnup

Topics

Machine Learning > Application Areas > Data Augmentation Natural Language Processing > Applications > Machine Translation Machine Learning > Core Methods > Feature Selection Machine Learning > Learning Types > Data Augmentation

Keywords

low-resource language parallel corpus filtering preprocessing method scoring metric noisy parallel corpus low-resource machine translation noisy corpus
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