论文标题
滑板：用于编码结构化知识的自然语言接口
SKATE: A Natural Language Interface for Encoding Structured Knowledge
论文作者
论文摘要
在自然语言（NL）应用中，NL接口能够解释的内容与外行用户知道如何表达的内容之间通常存在不匹配。这项工作描述了一种新型的自然语言界面，该界面通过连续生成的半结构化模板来完善自然语言输入来减少这种不匹配。在本文中，我们描述了我们的方法如何使用神经语义解析器来解析NL输入并建议半结构化模板，这些模板被递归地填充以产生完全结构化的解释。我们还展示了滑板如何与神经产生模型集成，以交互暗示和获取常识性知识。我们为故事理解的任务提供了溜冰鞋的初步覆盖范围分析，然后在特定领域中描述该工具的当前业务用途：Covid-19策略设计。
In Natural Language (NL) applications, there is often a mismatch between what the NL interface is capable of interpreting and what a lay user knows how to express. This work describes a novel natural language interface that reduces this mismatch by refining natural language input through successive, automatically generated semi-structured templates. In this paper we describe how our approach, called SKATE, uses a neural semantic parser to parse NL input and suggest semi-structured templates, which are recursively filled to produce fully structured interpretations. We also show how SKATE integrates with a neural rule-generation model to interactively suggest and acquire commonsense knowledge. We provide a preliminary coverage analysis of SKATE for the task of story understanding, and then describe a current business use-case of the tool in a specific domain: COVID-19 policy design.
