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EqnMaster: Evaluating Mathematical Expressions with Generative Recurrent Networks

In this project, we seek to use computational models to infer mathematical structure from purely symbolic inputs. Though there are existing systems for parsing mathematical expressions, there have been only limited approaches to apply learning-based algorithms to this inherently subtle task. This project therefore proposes an integrated methodology that applies various configurations of neural networks to analyze sequences of mathematical language. We define both a discriminative task – in which we pursue the verification of a given equation – and a generative task – where we predict the evaluation of an input mathematical expression. We test our models on a novel dataset, and gain insight into the workings of our models by learning underlying representations of our data.