NumNet: Machine Reading Comprehension with Numerical Reasoning release_p5ntlou32vc7hhdtz2zrz2d25y

by Qiu Ran, Yankai Lin, Peng Li, Jie Zhou, Zhiyuan Liu

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2019  

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.
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Date   2019-10-15
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arXiv  1910.06701v1
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