DrQA 是一个阅读理解系统用在开放领域问答。特别的，DrQA 针对一个机器阅读任务。在这个列表里，我们为一个潜在非常大的预料库中搜索一个问题的答案。所以，这个系统必须结合文本检索和机器文本理解。
项目由 https://github.com/facebookresearch 发布。项目地址：https://github.com/facebookresearch/DrQA
DrQA is a system for reading comprehension applied to open-domain question answering. In particular, DrQA is targeted at the task of "machine reading at scale" (MRS). In this setting, we are searching for an answer to a question in a potentially very large corpus of unstructured documents (that may not be redundant). Thus the system has to combine the challenges of document retrieval (finding the relevant documents) with that of machine comprehension of text (identifying the answers from those documents).
Our experiments with DrQA focus on answering factoid questions while using Wikipedia as the unique knowledge source for documents. Wikipedia is a well-suited source of large-scale, rich, detailed information. In order to answer any question, one must first retrieve the few potentially relevant articles among more than 5 million, and then scan them carefully to identify the answer.
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