论文标题
自然语言质量质量质量检查方法使用外部知识推理
Natural Language QA Approaches using Reasoning with External Knowledge
论文作者
论文摘要
从自然语言（NL）中的问题回答（QA）是AI早期的重要方面。温格拉德（Winograd）的``议员''在1972年的论文和麦卡锡（McCarthy）的1976年的Hug示例中强调了外部知识在NL理解中的作用。在过去30年中，机器学习一直是NL处理中的首选方法以及NL问题回答（NLQA），但最近在NLQA上越来越强调线程，其中外部知识起着重要作用。 Winograd的议员示例启发的挑战，以及最近的发展，例如重新启动AI书籍，各种NLQA数据集，NLQA环境中知识获取的研究以及它们在各种NLQA模型中的使用，都将NLQA带来了NLQA的问题，该问题使用了“推理”，并带有“推理”，并带来了Firsfront的外部知识。在本文中，我们介绍了最近对它们的工作的调查。我们认为，我们的调查将有助于在AI的多个领域之间建立桥梁，尤其是（a）（a）知识代表和推理的传统领域以及（b）NL理解和NLQA领域。
Question answering (QA) in natural language (NL) has been an important aspect of AI from its early days. Winograd's ``councilmen'' example in his 1972 paper and McCarthy's Mr. Hug example of 1976 highlights the role of external knowledge in NL understanding. While Machine Learning has been the go-to approach in NL processing as well as NL question answering (NLQA) for the last 30 years, recently there has been an increasingly emphasized thread on NLQA where external knowledge plays an important role. The challenges inspired by Winograd's councilmen example, and recent developments such as the Rebooting AI book, various NLQA datasets, research on knowledge acquisition in the NLQA context, and their use in various NLQA models have brought the issue of NLQA using ``reasoning'' with external knowledge to the forefront. In this paper, we present a survey of the recent work on them. We believe our survey will help establish a bridge between multiple fields of AI, especially between (a) the traditional fields of knowledge representation and reasoning and (b) the field of NL understanding and NLQA.
