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Analyzing Assumptions in Conversation Disentanglement Research Through the Lens of a New Dataset and Model

Jonathan K. Kummerfeld,S. R. Gouravajhala,Joseph Peper,V. Athreya,R. Chulaka Gunasekara,Jatin Ganhotra,Siva Sankalp Patel,L. Polymenakos,Walter S. Lasecki

Published 2018 in arXiv.org

ABSTRACT

Disentangling conversations mixed together in a single stream of messages is a difficult task with no large annotated datasets. We created a new dataset that is 25 times the size of any previous publicly available resource, has samples of conversation from 152 points in time across a decade, and is annotated with both threads and a within-thread reply-structure graph. We also developed a new neural network model, which extracts conversation threads substantially more accurately than prior work. Using our annotated data and our model we tested assumptions in prior work, revealing major issues in heuristically constructed resources, and identifying how small datasets have biased our understanding of multi-party multi-conversation chat.

PUBLICATION RECORD

  • Publication year

    2018

  • Venue

    arXiv.org

  • Publication date

    2018-10-25

  • Fields of study

    Computer Science

  • Identifiers
    arXiv 1810.11118
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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