SelQA

SelQA是一个新的选择题问答数据集，由埃默里大学创建。该数据集包含从英文维基百科的十大流行主题中提取的问题和答案，通过众包方式生成。数据集的创建过程涉及一系列众包任务，旨在减少问题和答案之间的词汇共现，从而生成大规模、多样化和具有挑战性的数据集。SelQA数据集主要用于评估开放领域问答系统的阅读理解能力，特别是在答案句选择和答案触发任务中。

SelQA is a novel multiple-choice question answering (QA) dataset created by Emory University. It consists of questions and answers extracted from ten popular topics in English Wikipedia, generated via crowdsourcing. The dataset construction involves a series of crowdsourcing tasks, which aim to reduce lexical co-occurrences between questions and answers, thereby producing a large-scale, diverse and challenging dataset. The SelQA dataset is primarily used to evaluate the reading comprehension capabilities of open-domain question answering systems, particularly in answer sentence selection and answer triggering tasks.