@inproceedings{zou-lu-2019-quantity,
    title = "Quantity Tagger: A Latent-Variable Sequence Labeling Approach to Solving Addition-Subtraction Word Problems",
    author = "Zou, Yanyan  and
      Lu, Wei",
    editor = "Korhonen, Anna  and
      Traum, David  and
      M{\`a}rquez, Llu{\'i}s",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/P19-1517/",
    doi = "10.18653/v1/P19-1517",
    pages = "5246--5251",
    abstract = "An arithmetic word problem typically includes a textual description containing several constant quantities. The key to solving the problem is to reveal the underlying mathematical relations (such as addition and subtraction) among quantities, and then generate equations to find solutions. This work presents a novel approach, \textit{Quantity Tagger}, that automatically discovers such hidden relations by tagging each quantity with a \textit{sign} corresponding to one type of mathematical operation. For each quantity, we assume there exists a latent, variable-sized \textit{quantity span} surrounding the quantity token in the text, which conveys information useful for determining its sign. Empirical results show that our method achieves 5 and 8 points of accuracy gains on two datasets respectively, compared to prior approaches."
}