systems on the sentence selection subtask.

In comparison to the related methods from above that generate questions from raw text, our method is different in its ability to take into account contextual information beyond the sentence-level by introducing coreference knowledge.

Question Answering Datasets and Creation
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Recently there has been an increasing interest in question answering with the creation of many datasets. Most are built using crowdsourcing; they are generally comprised of fewer than 100,000 QA pairs and are time-consuming to create. WebQuestions [@berant2013semantic], for example, contains 5,810 questions crawled via the Google Suggest API and is designed for knowledge base QA with answers restricted to Freebase entities. To tackle the size issues associated with WebQuestions,  introduce SimpleQuestions, a dataset of 108,442 questions authored by English speakers. SQuAD [@rajpurkar2016squad] is a dataset for machine comprehension; it is created by showing a Wikipedia paragraph to human annotators and asking them to write questions based on the paragraph. TriviaQA [@JoshiTriviaQA2017] includes 95k question-answer authored by trivia enthusiasts and corresponding evidence documents. (Semi-)synthetic generated datasets are easier to build to large-scale [@hill2015goldilocks; @hermann2015teaching]. They usually come in the form of cloze-style questions. For example,  created over a million examples by pairing CNN and 