# Data-Driven Methods for Solving Algebra Word Problems

Published in arxiv, 2018

Abstract:

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.

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Bibtex:

@misc{robaidek2018datadriven,
title={Data-Driven Methods for Solving Algebra Word Problems},
author={Benjamin Robaidek and Rik Koncel-Kedziorski and Hannaneh Hajishirzi},
year={2018},
eprint={1804.10718},
archivePrefix={arXiv},
primaryClass={cs.AI}
}