告诉我如何再问一次：连续空间中可控重写的数据增强问题
Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space
摘要：
在本文中，我们提出了一种新的数据增强方法，称为可控重写基于问题数据增强（CRQDA），机器阅读理解（MRC），问题生成，问答自然语言推理任务。我们把问题数据增强任务作为一个受约束的问题重写问题，以生成上下文相关的，高质量的，和多样化的问题数据样本。CRQDA利用Transformer自动编码器将原始离散问题映射到连续嵌入空间。然后，它使用预先训练的MRC模型通过基于梯度的优化迭代地修改问题表示。最后，修改后的问题表示被映射回离散空间，作为额外的问题数据。在SQuAD 2.0、SQuAD 1.1问题生成和QNLI任务上的综合实验证明了CRQDA的有效性
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
