Human understanding of information represented in natural versus artificial language (Poster)

Erin G. Zaroukian, J. Bakdash
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Abstract

In this paper we compare human understanding of information represented in a natural language (NL) to a type of artificial language, called a Controlled Natural Language (CNL). Potential applications for CNLs include decision support and conversational agents, but currently there is limited empirical research on the understandability of CNLs for untrained humans. We investigate a particular type of CNL, called Controlled English (CE), which was designed to be a simplified, artificial subset of natural language that is both human readable and unambiguous for fast and accurate machine processing. We quantify and compare human understanding of NL and CE using accuracy and speed for language statements. The statements described entities (people and objects) and relations (actions) among entities with the ground-truth represented using visual diagrams. Participants responded whether the statements matched the diagram (yes/no). In Experiment I, we found accuracy for NL and CE was comparable, although the speed for understanding CE was slower. To further examine the role of speed, we induced time pressure in Experiment II. We found both the accuracy and speed for CE was lower than NL. These results indicate that if people have sufficient time, understanding for CE can be equivalent to NL. However, with limited time the accuracy and speed for understanding NL is better than CE. Our findings indicate that both accuracy and speed of CNLs should be evaluated. Furthermore, under time pressure there can be meaningful differences in accuracy and speed between different ways of representing information. Understanding for methods of representing machine information has potential implications for situation understanding and management with human-machine interaction and collaboration.
人类对自然语言与人工语言所表示的信息的理解(海报)
在本文中,我们比较了人类对自然语言(NL)和一种被称为受控自然语言(CNL)的人工语言的理解。cnl的潜在应用包括决策支持和会话代理,但目前关于未经训练的人类对cnl的可理解性的实证研究有限。我们研究了一种特殊类型的CNL,称为受控英语(CE),它被设计为自然语言的简化人工子集,既可读又明确,用于快速准确的机器处理。我们量化和比较人类对NL和CE的理解,使用语言陈述的准确性和速度。这些陈述描述实体(人和物体)和实体之间的关系(动作),并使用可视化图表表示基本事实。参与者回答这些陈述是否与图表相匹配(是/否)。在实验1中,我们发现NL和CE的准确率是相当的,尽管理解CE的速度较慢。为了进一步研究速度的作用,我们在实验二中引入了时间压力。我们发现CE的准确度和速度都低于NL。这些结果表明,如果人们有足够的时间,对CE的理解可以等同于对NL的理解。然而,在有限的时间内,理解NL的准确性和速度优于CE。我们的研究结果表明cnl的准确性和速度都需要评估。此外,在时间压力下,不同的信息表达方式在准确性和速度上可能存在显著差异。理解表示机器信息的方法对人机交互和协作的情况理解和管理具有潜在的意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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