Are Machine Reading Comprehension Systems Robust to Context Paraphrasing?

Abstract

Investigating the behaviour of Machine Reading Comprehension (MRC) models under various types of test-time perturbations can shed light on the enhancement of their robustness and generalisation capability, despite the su-perhuman performance they have achieved on existing benchmark datasets. In this paper, we study the robustness of contemporary MRC systems to context paraphrasing, i.e., whether these models are still able to correctly answer the questions once the reading passages have been paraphrased. To this end, we systematically design a pipeline to semi-automatically generate perturbed MRC instances which ultimately lead to the creation of a paraphrased test set. We conduct experiments on this dataset with six state-of-the-art neural MRC models and we find that even the minimum performance drop of all these models exceeds 41%, whereas human performance remains high. Re-training models with augmented perturbed examples results in improved robustness, though the performance remains lower than on the original dataset. These results demonstrate that the existing high-performing MRC systems are still far away from real language understanding 1 .