Generating Near and Far Analogies for Educational Applications: Progress and Challenges

Abstract

Analogical reasoning, it has been argued, fundamentally underlies many cognitive processes and is an important marker of developmental cognition. This connection suggests that the clever use of analogical reasoning tasks can improve cognitive performance in specific ways, thus leading to clear educational applications, as recent psychological work has confirmed. However, currently there are no known methods to either solve or generate analogical word problems, at least to a degree of reliability that would be necessary before such educational applications are possible. To address these concerns we present work to both solve and generate analogy word problems: First, given an analogy word problem, our algorithm performs a parallel random walk through the semantic network ConceptNet to limit the number of choices that are then considered by a vector embedding. We achieve an improvement in accuracy beyond existing state-of-the-art. Second, we explore a method for automatically generating explainable n-step analogy word problems, and analyze the results.