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2019 ACL ACL 2019

Dual Monolingual Cross-Entropy Delta Filtering of Noisy Parallel Data

Abstract

AbstractWe introduce a purely monolingual approach to filtering for parallel data from a noisy corpus in a low-resource scenario. Our work is inspired by Junczysdowmunt:2018, but we relax the requirements to allow for cases where no parallel data is available. Our primary contribution is a dual monolingual cross-entropy delta criterion modified from Cynical data selection Axelrod:2017, and is competitive (within 1.8 BLEU) with the best bilingual filtering method when used to train SMT systems. Our approach is featherweight, and runs end-to-end on a standard laptop in three hours.

🌉 Interdisciplinary Bridge — Data Science & Analytics and Interdisciplinary and Machine Learning and Natural Language Processing
🧭 Keyword Pioneer — noisy data filtering
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Speech & Audio

Authors

Amittai Axelrod , Anish Kumar , Steve Sloto

Topics

Machine Learning > Learning Types > Unsupervised Learning Machine Learning > Optimization & Theory > Optimization Natural Language Processing > Applications > Machine Translation Data Science & Analytics > Methods > Data Mining Interdisciplinary > Linguistics > Computational Linguistics Machine Learning > Learning Types > Data Augmentation

Keywords

statistical machine translation noise filtering cross entropy parallel corpus filtering low-resource scenario noisy data filtering parallel data filtering low resource mt noisy corpus cross-entropy delta monolingual approach monolingual filtering
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