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In this paper, we propose a novel data augmentation method, referred to asControllable Rewriting based Question Data Augmentation (CRQDA), for machinereading comprehension (MRC), question generation, and question-answeringnatural language inference tasks. We treat the question data augmentation taskas a constrained question rewriting problem to generate context-relevant,high-quality, and diverse question data samples. CRQDA utilizes a Transformerautoencoder to map the original discrete question into a continuous embeddingspace. It then uses a pre-trained MRC model to revise the questionrepresentation iteratively with gradient-based optimization. Finally, therevised question representations are mapped back into the discrete space, whichserve as additional question data. Comprehensive experiments on SQuAD 2.0,SQuAD 1.1 question generation, and QNLI tasks demonstrate the effectiveness ofCRQDA

Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space