@inproceedings{ran-etal-2019-numnet,
    title = "{N}um{N}et: Machine Reading Comprehension with Numerical Reasoning",
    author = "Ran, Qiu  and
      Lin, Yankai  and
      Li, Peng  and
      Zhou, Jie  and
      Liu, Zhiyuan",
    editor = "Inui, Kentaro  and
      Jiang, Jing  and
      Ng, Vincent  and
      Wan, Xiaojun",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/D19-1251/",
    doi = "10.18653/v1/D19-1251",
    pages = "2474--2484",
    abstract = "Numerical reasoning, such as addition, subtraction, sorting and counting is a critical skill in human`s reading comprehension, which has not been well considered in existing machine reading comprehension (MRC) systems. To address this issue, we propose a numerical MRC model named as NumNet, which utilizes a numerically-aware graph neural network to consider the comparing information and performs numerical reasoning over numbers in the question and passage. Our system achieves an EM-score of 64.56{\%} on the DROP dataset, outperforming all existing machine reading comprehension models by considering the numerical relations among numbers."
}