@inproceedings{malmasi-etal-2017-unsupervised,
    title = "Unsupervised Text Segmentation Based on Native Language Characteristics",
    author = "Malmasi, Shervin  and
      Dras, Mark  and
      Johnson, Mark  and
      Du, Lan  and
      Wolska, Magdalena",
    editor = "Barzilay, Regina  and
      Kan, Min-Yen",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/P17-1134/",
    doi = "10.18653/v1/P17-1134",
    pages = "1457--1469",
    abstract = "Most work on segmenting text does so on the basis of topic changes, but it can be of interest to segment by other, stylistically expressed characteristics such as change of authorship or native language. We propose a Bayesian unsupervised text segmentation approach to the latter. While baseline models achieve essentially random segmentation on our task, indicating its difficulty, a Bayesian model that incorporates appropriately compact language models and alternating asymmetric priors can achieve scores on the standard metrics around halfway to perfect segmentation."
}