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.
Cite
CITATION STYLE
Ran, Q., Lin, Y., Li, P., Zhou, J., & Liu, Z. (2019). NumNet: Machine reading comprehension with numerical reasoning. In EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference (pp. 2474–2484). Association for Computational Linguistics. https://doi.org/10.18653/v1/d19-1251
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