  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.
