NumNet: Machine Reading Comprehension with Numerical Reasoning
release_p5ntlou32vc7hhdtz2zrz2d25y
by
Qiu Ran, Yankai Lin, Peng Li, Jie Zhou, Zhiyuan Liu
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.
In text/plain
format
Archived Files and Locations
application/pdf 380.6 kB
file_3yowbrme25ahbmdlcer3rn65h4
|
arxiv.org (repository) web.archive.org (webarchive) |
1910.06701v1
access all versions, variants, and formats of this works (eg, pre-prints)