论文标题
人们在问Covid-19的什么人是什么？一个问题分类数据集
What Are People Asking About COVID-19? A Question Classification Dataset
论文作者
论文摘要
我们提出了Covid-Q，这是1690个关于Covid-19的问题，来自13个来源，我们将其注释为15个问题类别和207个问题簇。我们数据集中最常见的问题询问了Covid的传播，预防和社会影响，我们发现在CDC和FDA等知名组织的任何FAQ网站上没有回答许多出现在多个来源中的问题。我们在https://github.com/jerryweiai/covid-q公开发布数据集。对于将问题分类为15个类别，BERT基线在每个类别的20个示例中接受培训时得分为58.1％，对于问题聚类任务，BERT + TRIPLET损失基线的精度为49.5％。我们希望Covid-Q可以帮助直接用于开发应用系统或作为模型评估的领域特定资源。
We present COVID-Q, a set of 1,690 questions about COVID-19 from 13 sources, which we annotate into 15 question categories and 207 question clusters. The most common questions in our dataset asked about transmission, prevention, and societal effects of COVID, and we found that many questions that appeared in multiple sources were not answered by any FAQ websites of reputable organizations such as the CDC and FDA. We post our dataset publicly at https://github.com/JerryWeiAI/COVID-Q. For classifying questions into 15 categories, a BERT baseline scored 58.1% accuracy when trained on 20 examples per category, and for a question clustering task, a BERT + triplet loss baseline achieved 49.5% accuracy. We hope COVID-Q can help either for direct use in developing applied systems or as a domain-specific resource for model evaluation.
