Adversarial training (AT) as a regularization method has proved its effectiveness on various tasks. Though there are successful applications of AT on some NLP tasks, the distinguishing characteristics of NLP tasks have not been exploited. In this paper, we aim to apply AT on machine reading comprehension (MRC) tasks. Furthermore, we adapt AT for MRC tasks by proposing a novel adversarial training method called PQAT that perturbs the embedding matrix instead of word vectors. To differentiate the roles of passages and questions, PQAT uses additional virtual P/Q-embedding matrices to gather the global perturbations of words from passages and questions separately. We test the method on a wide range of MRC tasks, including span-based extractive RC and multiple-choice RC. The results show that adversarial training is effective universally, and PQAT further improves the performance.

翻译：作为一种正规化方法,对口培训(AT)已证明在各种任务上是有效的,尽管AT成功地应用了国家劳工政策的一些任务,但是没有利用国家劳工政策任务的特征;在本文件中,我们的目标是将AT应用于机器阅读(MRC)任务;此外,我们通过提议一种称为PQAT的新型对抗性培训方法,来对嵌入矩阵而不是语言矢量进行干扰,使AT适应MRC任务;为了区分通道和问题的作用,PQAT使用额外的虚拟P/Q组合矩阵,分别收集从通道和问题中出现的词的全球扰动。我们测试该方法涉及广泛的MRC任务,包括跨边界采掘驻地协调员和多选择RC。结果显示,对抗性培训是有效的,PQAT进一步提高了业绩。