This paper presents a new selection-based question answering dataset, SelQA.
The dataset consists of questions generated through crowdsourcing and sentence
length answers that are drawn from the ten most prevalent topics in the English
Wikipedia. We introduce a corpus annotation scheme that enhances the generation
of large, diverse, and challenging datasets by explicitly aiming to reduce word
co-occurrences between the question and answers. Our annotation scheme is
composed of a series of crowdsourcing tasks with a view to more effectively
utilize crowdsourcing in the creation of question answering datasets in various
domains. Several systems are compared on the tasks of answer sentence selection
and answer triggering, providing strong baseline results for future work to
improve upon.

本篇论文提出了一个新的选择题型问答数据集 SelQA，该数据集通过众包生成问题，并从英文维基百科中提取十个最常见主题的长度为答案。我们介绍了一种语料库注释方案，旨在通过明确减少问题和答案之间的词共现来增强生成大型、多样化和具有挑战性的数据集的过程。在回答句子选择和回答触发任务上，我们比较了几个系统，为未来的工作提供了强有力的基准结果。