The CMU-Avenue French-English Translation System

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Parallel Corpus Filtering via Pre-trained Language Models (2020, methodology): “Denkowski et al. (2012), Dyer et al. (2010) and Heaﬁeld (2011) use language models and word alignments to determine how likely sentences are to be a good translation of another.”
An Unsupervised System for Parallel Corpus Filtering (2018, methodology): “Similarly, word alignments and language modeling were used in (Denkowski et al., 2012) to select sentence pairs that are useful for training an MT system.”
Alibaba Submission to the WMT18 Parallel Corpus Filtering Task (2018, methodology): “Besides, the language model can also be used to ﬁlter out ungrammatical data (Denkowski et al., 2012; Al-lauzen et al., 2011), which is suitable for this task.”
The JHU Parallel Corpus Filtering Systems for WMT 2018 (2018, methodology): “Similar to this work, the qe-clean system (Denkowski et al., 2012; Dyer et al., 2010; Heaﬁeld, 2011) uses word alignments and language models to select sentence pairs that are likely to be good translations of one another.”
Findings of the 2012 Workshop on Statistical Machine Translation (2012, background): “CMU Carnegie Mellon University (Denkowski et al., 2012) CU-BOJAR Charles University - Bojar (Bojar et al.”
The CMU Machine Translation Systems at WMT 2014 (2014, methodology): “Our data was ﬁltered using qe-clean (Denkowski et al., 2012), with a cutoff of two standard deviations from the mean.”