@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://aclanthology.org/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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T NumNet: Machine Reading Comprehension with Numerical Reasoning
%A Ran, Qiu
%A Lin, Yankai
%A Li, Peng
%A Zhou, Jie
%A Liu, Zhiyuan
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F ran-etal-2019-numnet
%X 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.
%R 10.18653/v1/D19-1251
%U https://aclanthology.org/D19-1251
%U https://doi.org/10.18653/v1/D19-1251
%P 2474-2484
Markdown (Informal)
[NumNet: Machine Reading Comprehension with Numerical Reasoning](https://aclanthology.org/D19-1251) (Ran et al., EMNLP-IJCNLP 2019)
ACL
- Qiu Ran, Yankai Lin, Peng Li, Jie Zhou, and Zhiyuan Liu. 2019. NumNet: Machine Reading Comprehension with Numerical Reasoning. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2474–2484, Hong Kong, China. Association for Computational Linguistics.