The vast and fast-growing STEM literature makes it imperative to develop systems for automated math-semantics extraction from technical content, and for semantically-enabled processing of such content. As math and science involve sequences of text, symbols and equations, deep learning models are expected to deliver good performance in math-semantics extraction and processing.
This project keeps things simple with addition of two numbers, but we can see how this may be scaled to a variable number of terms and mathematical operations that could be given as input for the model to learn and generalize.