TITLE: DuReader_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World Applications 

ABSTRACT: Machine reading comprehension (MRC) is a crucial task in natural language
processing and has achieved remarkable advancements. However, most of the
neural MRC models are still far from robust and fail to generalize well in
real-world applications. In order to comprehensively verify the robustness and
generalization of MRC models, we introduce a real-world Chinese dataset --
DuReader_robust. It is designed to evaluate the MRC models from three aspects:
over-sensitivity, over-stability and generalization. Comparing to previous
work, the instances in DuReader_robust are natural texts, rather than the
altered unnatural texts. It presents the challenges when applying MRC models to
real-world applications. The experimental results show that MRC models do not
perform well on the challenge test set. Moreover, we analyze the behavior of
existing models on the challenge test set, which may provide suggestions for
future model development. The dataset and codes are publicly available at
https://github.com/baidu/DuReader.
Machine reading comprehension (MRC) is a crucial task in natural language
processing and has achieved remarkable advancements. However, most of the
neural MRC models are still far from robust and fail to generalize well in
real-world applications. In order to comprehensively verify the robustness and
generalization of MRC models, we introduce a real-world Chinese dataset --
DuReader_robust. It is designed to evaluate the MRC models from three aspects:
over-sensitivity, over-stability and generalization. Comparing to previous
work, the instances in DuReader_robust are natural texts, rather than the
altered unnatural texts. It presents the challenges when applying MRC models to
real-world applications. The experimental results show that MRC models do not
perform well on the challenge test set. Moreover, we analyze the behavior of
existing models on the challenge test set, which may provide suggestions for
future model development. The dataset and codes are publicly available at
https://github.com/baidu/DuReader.
DuReader_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World Applications
http://arxiv.org/abs/2004.11142v2