MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms
This work addresses the problem of interpretability and scalability in math word problem solving for AI research, but it is incremental as it builds on existing datasets and methods.
The authors tackled the challenge of interpretable math word problem solving by introducing a large-scale dataset, MathQA, with operation-based formalisms, and a neural sequence-to-program model that improved performance over baselines, though still significantly below human levels.
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/