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Quizbowl: The Case for Incremental Question Answering

Pedro Rodriguez,Shi Feng,Mohit Iyyer,He He,J. Boyd-Graber

Published 2019 in arXiv.org

ABSTRACT

Quizbowl is a scholastic trivia competition that tests human knowledge and intelligence; additionally, it supports diverse research in question answering (QA). A Quizbowl question consists of multiple sentences whose clues are arranged by difficulty (from obscure to obvious) and uniquely identify a well-known entity such as those found on Wikipedia. Since players can answer the question at any time, an elite player (human or machine) demonstrates its superiority by answering correctly given as few clues as possible. We make two key contributions to machine learning research through Quizbowl: (1) collecting and curating a large factoid QA dataset and an accompanying gameplay dataset, and (2) developing a computational approach to playing Quizbowl that involves determining both what to answer and when to answer. Our Quizbowl system has defeated some of the best trivia players in the world over a multi-year series of exhibition matches. Throughout this paper, we show that collaborations with the vibrant Quizbowl community have contributed to the high quality of our dataset, led to new research directions, and doubled as an exciting way to engage the public with research in machine learning and natural language processing.

PUBLICATION RECORD

  • Publication year

    2019

  • Venue

    arXiv.org

  • Publication date

    2019-04-09

  • Fields of study

    Computer Science

  • Identifiers
    arXiv 1904.04792
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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