Class-based N-gram language difference models for data selection

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Zipporah: a Fast and Scalable Data Cleaning System for Noisy Web-Crawled Parallel Corpora (2017, methodology): “Axelrod et al. (2015) utilized part-of-speech tags and used a class-based n-gram language model for selecting in-domain data.”
The JHU Parallel Corpus Filtering Systems for WMT 2018 (2018, methodology): “Axelrod et al. (2015) utilized part-of-speech tags and used a class-based n-gram language model for selecting in-domain data and Duh et al. (2013) used a neural network based language model trained on a small in-domain corpus to select from a larger mixed-domain data pool.”
Back-Translation Sampling by Targeting Difficult Words in Neural Machine Translation (2018, methodology): “(2017) experimented with using domain adaptation methods to select monolingual data based on the cross-entropy between the monolingual data and in-domain corpus (Axelrod et al., 2015) but did not find any improvements over random sampling as originally proposed by Sennrich et al.”
Data Selection with Cluster-Based Language Difference Models and Cynical Selection (2019, methodology): “These followed the steps in [1], with every word replaced by a new token consisting of a cluster label and a bias suffix.”