Automatic fact extraction from open-ended geometry questions

Abstract

Semantic parsing of geometry problems is the first step towards automated geometry problem solvers. Existing systems for this task heavily depend on language-specific NLP tools and use hard-coded parsing rules. Moreover, these systems produce a static set of facts and record low precision scores. In this study, we present the two-step memory network, a novel neural network architecture for deep semantic parsing of GWPs. Our model is language independent and optimized for low-resource domains. Without using any language-specific NLP tool, our system performs as good as existing systems. We also introduce on-demand fact extraction, where a solver can query the model about entities during the solving stage. This is impossible for existing systems; the set of extracted facts with these systems are static after the parsing stage. This feature alleviates the problem of having an imperfect recall. We also investigate data augmentation techniques for low resource domains to alleviate the difficulties in applying deep learning techniques in the domain above. We also introduce an enhanced metric for evaluating language generative models alleviating the the limitations of exiting metrics. Analysing the results, we come up with a ranking of models on their suitability to be used o low resource domains