问答调查：方向、挑战、数据集、评价矩阵

在过去的十年中，互联网上现有信息的使用量和数量都有所增加。这种数字化导致自动应答系统从冗余和过渡的知识来源中提取富有成效的信息。这些系统的设计是为了满足这个巨大的知识源对使用自然语言理解(NLU)的用户查询的最突出的答案，因此非常依赖于问答(QA)字段。问答环节包括但不限于将用户问题映射到相关查询、检索相关信息、从检索到的信息中找到最合适的答案等步骤。目前对深度学习模型的改进表明，在所有这些任务中都有了显著的性能提高。

本文综述了问题类型、答案类型、证据来源和建模方法，分析了Q保证领域的研究方向。这详细说明了该领域的公开挑战，如自动问题生成、相似性检测以及一种语言的低资源可用性。最后，对现有的数据集和评价措施进行了调查。

原文题目：Question Answering Survey: Directions, Challenges, Datasets, Evaluation Matrices

原文：The usage and amount of information available on the internet increase over the past decade. This digitization leads to the need for automated answering system to extract fruitful information from redundant and transitional knowledge sources. Such systems are designed to cater the most prominent answer from this giant knowledge source to the user query using natural language understanding (NLU) and thus eminently depends on the Question-answering(QA) field. Question answering involves but not limited to the steps like mapping of user question to pertinent query, retrieval of relevant information, finding the best suitable answer from the retrieved information etc. The current improvement of deep learning models evince compelling performance improvement in all these tasks. In this review work, the research directions of QA field are analyzed based on the type of question, answer type, source of evidence-answer, and modeling approach. This detailing followed by open challenges of the field like automatic question generation, similarity detection and, low resource availability for a language. In the end, a survey of available datasets and evaluation measures is presented.