Data-Driven Methods for Solving Algebra Word Problems

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}
}