Attention virtual adversarial based semi-supervised question generation

Question generation (QG) refers to the automatic generation of questions based on the given passages and answers, and has a wide range of application scenarios in human-computer interaction, education, medical and other fields. However, for Chinese QG, due to the lack of word separation in the writing rules of Chinese text, many methods are not suitable for it, and the generated results have incorrect word order and invalid expressions. In addition, traditional models only use labeled data, but it is difficult and expensive to obtain data labels. In order to solve this problem, this article proposes a semi-supervised QG model termed virtual stroke-aware copy network (VSAC Net). It is based on virtual adversarial training and can be used for Chinese QG tasks with few labeled samples. The VSAC Net model combines word embedding virtual counter disturbance and attention virtual counter disturbance, the fitting of the input layer and the attention layer is taken into account, and reduces the overfitting of the model. According to Dureader dataset, a small sample QG dataset is constructed, and the VSAC Net is used for solving. The results show that the proposed model can achieve a better generation effect on small sample datasets.