jkkummerfeld/irc_disentangle

Tasks

• token-classification

Citation text from the card

@inproceedings{kummerfeld-etal-2019-large,
    title = "A Large-Scale Corpus for Conversation Disentanglement",
    author = "Kummerfeld, Jonathan K.  and
      Gouravajhala, Sai R.  and
      Peper, Joseph J.  and
      Athreya, Vignesh  and
      Gunasekara, Chulaka  and
      Ganhotra, Jatin  and
      Patel, Siva Sankalp  and
      Polymenakos, Lazaros C  and
      Lasecki, Walter",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P19-1374",
    doi = "10.18653/v1/P19-1374",
    pages = "3846--3856",
    arxiv = "https://arxiv.org/abs/1810.11118",
    software = "https://jkk.name/irc-disentanglement",
    data = "https://jkk.name/irc-disentanglement",
    abstract = "Disentangling conversations mixed together in a single stream of messages is a difficult task, made harder by the lack of large manually annotated datasets. We created a new dataset of 77,563 messages manually annotated with reply-structure graphs that both disentangle conversations and define internal conversation structure. Our data is 16 times larger than all previously released datasets combined, the first to include adjudication of annotation disagreements, and the first to include context. We use our data to re-examine prior work, in particular, finding that 89{\%} of conversations i

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