Understand
In this work we introduce dual conditional cross-entropy filtering for noisy parallel data.
- For each sentence pair of the noisy parallel corpus we compute cross-entropy scores according to two inverse translation models trained on clean data.
- We penalize divergent cross-entropies and weigh the penalty by the cross-entropy average of both models.
- Sorting or thresholding according to these scores results in better subsets of parallel data.
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Moses: Open source toolkit for statistical machine translation
Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Chris Dyer, Ondrej Bojar, Alexandra Constantin, and Evan Herbst. 2007 · 2007
Earlier work this paper cites.
Intelligent selection of language model training data
Robert C. Moore and William Lewis. 2010 · 2010
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Domain adaptation via pseudo in-domain data selection
Amittai Axelrod, Xiaodong He, and Jianfeng Gao. 2011 · 2011
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A theoretically grounded application of dropout in recurrent neural networks
Yarin Gal and Zoubin Ghahramani. 2016 · 2016
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