  In this paper, we propose a novel data augmentation method, referred to as
Controllable Rewriting based Question Data Augmentation (CRQDA), for machine
reading comprehension (MRC), question generation, and question-answering
natural language inference tasks. We treat the question data augmentation task
as a constrained question rewriting problem to generate context-relevant,
high-quality, and diverse question data samples. CRQDA utilizes a Transformer
autoencoder to map the original discrete question into a continuous embedding
space. It then uses a pre-trained MRC model to revise the question
representation iteratively with gradient-based optimization. Finally, the
revised question representations are mapped back into the discrete space, which
serve as additional question data. Comprehensive experiments on SQuAD 2.0,
SQuAD 1.1 question generation, and QNLI tasks demonstrate the effectiveness of
CRQDA
