论文标题
在低资源模板提取中问正确的问题
Asking the Right Questions in Low Resource Template Extraction
论文作者
论文摘要
信息提取（IE）研究人员正在映射任务以询问答案（QA），以利用现有的大型质量保证资源，从而提高数据效率。尤其是在模板提取（TE）中，将本体论映射到一组问题可能比收集标记的示例更具时间效率。我们询问TE系统的最终用户是否可以设计这些问题，以及将NLP从业人员参与此过程是否有益。我们将问题与其他措辞自然语言提示提示TE进行比较。我们提出了一个新颖的模型，可以用提示执行TE，并发现它比其他提示的问题受益，并且它们不需要NLP背景来作者。
Information Extraction (IE) researchers are mapping tasks to Question Answering (QA) in order to leverage existing large QA resources, and thereby improve data efficiency. Especially in template extraction (TE), mapping an ontology to a set of questions can be more time-efficient than collecting labeled examples. We ask whether end users of TE systems can design these questions, and whether it is beneficial to involve an NLP practitioner in the process. We compare questions to other ways of phrasing natural language prompts for TE. We propose a novel model to perform TE with prompts, and find it benefits from questions over other styles of prompts, and that they do not require an NLP background to author.
