Interpreting written how-to instructions

Abstract

Written instructions are a common way of teaching people how to accomplish tasks on the web. However, studies have shown that written instructions are difficult to follow, even for experienced users. A system that understands human-written instructions could guide users through the process of following the directions, improving completion rates and enhancing the user experience. While general natural language understanding is extremely difficult, we believe that in the limited domain of how-to instructions it should be possible to understand enough to provide guided help in a mixed-initiative environment. Based on a qualitative analysis of instructions gathered for 43 web-based tasks, we have formalized the problem of understanding and interpreting how-to instructions. We compare three different approaches to interpreting instructions: a keyword-based interpreter, a grammar-based interpreter, and an interpreter based on machine learning and information extraction. Our empirical results demonstrate the feasibility of automated how-to instruction understanding.