论文标题
一项关于知识基础回答复杂问题的调查：最近的进步和挑战
A Survey on Complex Question Answering over Knowledge Base: Recent Advances and Challenges
论文作者
论文摘要
关于知识库（KB）的问题回答（QA）旨在通过存储在知识库中的实体之间的结构良好的关系信息自动回答自然语言问题。为了使KBQA更适用于实际情况，研究人员将注意力从简单的问题转移到了复杂的问题，这些问题需要更多的KB三元组和约束推理。在本文中，我们介绍了复杂质量检查的最新进展。除了依靠模板和规则的传统方法外，该研究还归类为包含两个主要分支的分类法，即基于信息检索和基于神经语义解析。在描述了这些分支的方法之后，我们分析了未来研究的方向，并介绍了Alime团队提出的模型。
Question Answering (QA) over Knowledge Base (KB) aims to automatically answer natural language questions via well-structured relation information between entities stored in knowledge bases. In order to make KBQA more applicable in actual scenarios, researchers have shifted their attention from simple questions to complex questions, which require more KB triples and constraint inference. In this paper, we introduce the recent advances in complex QA. Besides traditional methods relying on templates and rules, the research is categorized into a taxonomy that contains two main branches, namely Information Retrieval-based and Neural Semantic Parsing-based. After describing the methods of these branches, we analyze directions for future research and introduce the models proposed by the Alime team.
