  We introduce a large-scale dataset of math word problems and an interpretable
neural math problem solver that learns to map problems to operation programs.
Due to annotation challenges, current datasets in this domain have been either
relatively small in scale or did not offer precise operational annotations over
diverse problem types. We introduce a new representation language to model
precise operation programs corresponding to each math problem that aim to
improve both the performance and the interpretability of the learned models.
Using this representation language, our new dataset, MathQA, significantly
enhances the AQuA dataset with fully-specified operational programs. We
additionally introduce a neural sequence-to-program model enhanced with
automatic problem categorization. Our experiments show improvements over
competitive baselines in our MathQA as well as the AQuA dataset. The results
are still significantly lower than human performance indicating that the
dataset poses new challenges for future research. Our dataset is available at:
https://math-qa.github.io/math-QA/
