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Title: What Question Answering can Learn from Trivia Nerds
In addition to the traditional task of machines answering questions, question answering (QA) research creates interesting, challenging questions that help systems how to answer questions and reveal the best systems. We argue that creating a QA dataset—and the ubiquitous leaderboard that goes with it—closely resembles running a trivia tournament: you write questions, have agents (either humans or machines) answer the questions, and declare a winner. However, the research community has ignored the hard-learned lessons from decades of the trivia community creating vibrant, fair, and effective question answering competitions. After detailing problems with existing QA datasets, we outline the key lessons—removing ambiguity, discriminating skill, and adjudicating disputes—that can transfer to QA research and how they might be implemented.  more » « less
Award ID(s):
1822494
PAR ID:
10212074
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Page Range / eLocation ID:
7422 to 7435
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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