What question answering can learn from trivia nerds

Abstract

Question answering (QA)is not just building systems; this NLP subfield also creates and curates challenging question datasets that reveal the best systems. We argue that QA datasets-and QA leaderboards-closely resemble trivia tournaments: the questions agents-humans or machines-answer reveals a “winner”. However, the research community has ignored the lessons from decades of the trivia community creating vibrant, fair, and effective QA competitions. After detailing problems with existing QA datasets, we outline several lessons that transfer to QA research: removing ambiguity, identifying better QA agents, and adjudicating disputes.