基于条件变分自编码器的问题生成方法

将条件变分自编码器作为辅助模块，引入预训练语言模型的编码解码过程，通过数据增强(潜在的语义扩充)以提高模型的鲁棒性。通过建立陈述句与疑问句之间的高维分布联系，由分布采样实现一对多的问题生成。结果表明，融合条件变分自编码器不仅能生成多样性的问题，也有助于提升问题生成的模型性能。在基于SQuAD数据集划分的2个答案可知问题生成数据集Split1和Split2上，BLEU-4值分别被提升到20.75%和21.61%。

Question generation method based on conditional variational autoencoder

The conditional variational autoencoder, as an auxiliary module, is introduced into the encoding and decoding process of the pre-trained language model. It improves the robustness of the model through data augmentation(potential semantic expansion), and establishes a high-dimensional distribution connection between declarative sentences and interrogative sentences, which implements one-to-many question generation by sampling from the distribution. The results show that the fusion of conditional variational autoencoder can not only generate diverse questions, but also help to improve the performance of the question generation model. On the two answer-known question generation datasets Split1 and Split2, which are based on the SQuAD dataset, the BLEU-4 score is improved to 20.75% and 21.61%, respectively.