论文标题
ZS4IE：用于零摄像信息提取的工具包，简单的口头化
ZS4IE: A toolkit for Zero-Shot Information Extraction with simple Verbalizations
论文作者
论文摘要
当前的信息提取工作流（IE）分析师涉及对实体/利益关系的定义以及带有带注释的示例的培训语料库。在此演示中，我们介绍了一个新的工作流程，分析师直接口头表达了实体/关系，然后由文本组成模型将其用于执行零摄像的IE。我们介绍了具有用户界面的工具包的设计和实现，以及对四个IE任务的实验，这些任务表明该系统在零照片学习时仅使用每种用户的努力就可以在零照片学习下实现非常好的性能。我们的演示系统在https://github.com/bbn-e/zs4ie上进行开源。可以在https://vimeo.com/676138340上找到演示视频。
The current workflow for Information Extraction (IE) analysts involves the definition of the entities/relations of interest and a training corpus with annotated examples. In this demonstration we introduce a new workflow where the analyst directly verbalizes the entities/relations, which are then used by a Textual Entailment model to perform zero-shot IE. We present the design and implementation of a toolkit with a user interface, as well as experiments on four IE tasks that show that the system achieves very good performance at zero-shot learning using only 5--15 minutes per type of a user's effort. Our demonstration system is open-sourced at https://github.com/BBN-E/ZS4IE . A demonstration video is available at https://vimeo.com/676138340 .
