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In this paper, we focus on studying robustness evaluation of Chinese questionmatching. Most of the previous work on analyzing robustness issue focus on justone or a few types of artificial adversarial examples. Instead, we argue thatit is necessary to formulate a comprehensive evaluation about the linguisticcapabilities of models on natural texts. For this purpose, we create a Chinesedataset namely DuQM which contains natural questions with linguisticperturbations to evaluate the robustness of question matching models. DuQMcontains 3 categories and 13 subcategories with 32 linguistic perturbations.The extensive experiments demonstrate that DuQM has a better ability todistinguish different models. Importantly, the detailed breakdown of evaluationby linguistic phenomenon in DuQM helps us easily diagnose the strength andweakness of different models. Additionally, our experiment results show thatthe effect of artificial adversarial examples does not work on the naturaltexts.

DuQM: A Chinese Dataset of Linguistically Perturbed Natural Questions for Evaluating the Robustness of Question Matching Models