Title: Towards Interpretable Math Word Problem Solving \\ with Operation-Based Formalisms
Advisors: Hannaneh Hajishirzi & Yejin Choi
Abstract: We introduce a large-scale dataset of math word problems and an interpretable neural math problem solver by learning to map problems to their 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 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, we significantly enhance the AQuA dataset with fully-specified operational programs. We additionally introduce a neural sequence-to-program model with automatic problem categorization. Our experiments show improvements over competitive baselines in our dataset 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.