All language model features are trained on the training proportion of each dataset, with 8-gram wordbased setting and Kneser-Ney smoothing (Kneser and Ney, 1995) trained by SRILM toolkit (Stolcke, 2002).
apart from releasing the dataset, they also proposed a methodology that anonymizes the named entity tokens in the data, and these tokens are also re-shuffle in each sample.