  We explore contemporary, data-driven techniques for solving math word
problems over recent large-scale datasets. We show that well-tuned neural
equation classifiers can outperform more sophisticated models such as sequence
to sequence and self-attention across these datasets. Our error analysis
indicates that, while fully data driven models show some promise, semantic and
world knowledge is necessary for further advances.
