Complex question answering in real world is a comprehensive and challenging task due to its demand for deeper question understanding and deeper inference. Information retrieval is a common solution and easy to implement, but it cannot answer questions which need long-distance dependencies across multiple documents. Knowledge base (KB) organizes information as a graph, and KB-based inference can employ logic formulas or knowledge embeddings to capture such long-distance semantic associations. However, KB-based inference has not been applied to real-world question answering well, because there are gaps among natural language, complex semantic structure, and appropriate hypothesis for inference. We propose decoupling KB-based inference by transforming a question into a high-level triplet in the KB, which makes it possible to apply KB-based inference methods to answer complex questions. In addition, we create a specialized question answering dataset only for inference, and our method is proved to be effective by conducting experiments on both AI2 Science Questions dataset and ours.

中文翻译：

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参加闭卷考试：通过虚拟假设解耦基于知识库的推理，以回答现实世界中的问题

现实世界中复杂的问题解答是一项全面而具有挑战性的任务，因为它需要更深入的问题理解和更深入的推理。信息检索是一种常见的解决方案，易于实现，但无法回答需要跨多个文档进行长距离依赖的问题。知识库（KB）将信息组织为图形，并且基于KB的推理可以采用逻辑公式或知识嵌入来捕获这种长距离语义关联。但是，基于知识库的推理尚未很好地应用于现实世界中的问题回答，因为自然语言，复杂的语义结构以及适当的推理假设之间存在差距。我们建议通过将问题转换为知识库中的高级三元组来解耦基于知识库的推理，这使得可以将基于知识库的推理方法应用于回答复杂问题。此外，我们创建了一个仅用于推理的专用问题回答数据集，并且通过对AI2科学问题数据集和我们的数据集进行了实验，证明了该方法是有效的。