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

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.