LiveQA：体育直播中的问答数据集
LiveQA: A Question Answering Dataset over Sports Live
摘要：
在本文中，我们介绍了LiveQA，一个新的问答数据集构建的逐场直播。它包含了117，000道由人类评论员为超过1，670场NBA比赛撰写的多项选择题，这些问题来自中国虎扑（https：//nba.hupu.com/games）网站。从体育比赛的特点出发，LiveQA可以测试基于时间轴的直播的推理能力，这与现有的数据集相比具有挑战性。在LiveQA中，这些问题需要理解时间轴，跟踪事件或进行数学计算。我们的初步实验表明，该数据集引入了一个具有挑战性的问题，问答模型，和一个强大的基线模型只能达到53.1%的准确率，不能击败主导选项规则。我们发布了本文的代码和数据，以供将来的研究。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
