@article{nishida-nakayama-2020-unsupervised,
    title = "Unsupervised Discourse Constituency Parsing Using {V}iterbi {EM}",
    author = "Nishida, Noriki  and
      Nakayama, Hideki",
    editor = "Johnson, Mark  and
      Roark, Brian  and
      Nenkova, Ani",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "8",
    year = "2020",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.tacl-1.15/",
    doi = "10.1162/tacl_a_00312",
    pages = "215--230",
    abstract = "In this paper, we introduce an unsupervised discourse constituency parsing algorithm. We use Viterbi EM with a margin-based criterion to train a span-based discourse parser in an unsupervised manner. We also propose initialization methods for Viterbi training of discourse constituents based on our prior knowledge of text structures. Experimental results demonstrate that our unsupervised parser achieves comparable or even superior performance to fully supervised parsers. We also investigate discourse constituents that are learned by our method."
}