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LiveQA: A Question Answering Dataset over Sports Live

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arxiv 2010.00526 v1 pith:4JLQC6D2 submitted 2020-10-01 cs.CL

LiveQA: A Question Answering Dataset over Sports Live

Qianying Liu , Sicong Jiang , Yizhong Wang , Sujian Li This is my paper
classification cs.CL
keywords liveqaansweringdatasetgameslivequestionchallenginghupu
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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In this paper, we introduce LiveQA, a new question answering dataset constructed from play-by-play live broadcast. It contains 117k multiple-choice questions written by human commentators for over 1,670 NBA games, which are collected from the Chinese Hupu (https://nba.hupu.com/games) website. Derived from the characteristics of sports games, LiveQA can potentially test the reasoning ability across timeline-based live broadcasts, which is challenging compared to the existing datasets. In LiveQA, the questions require understanding the timeline, tracking events or doing mathematical computations. Our preliminary experiments show that the dataset introduces a challenging problem for question answering models, and a strong baseline model only achieves the accuracy of 53.1\% and cannot beat the dominant option rule. We release the code and data of this paper for future research.

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