Questionator-Automated Question Generation using Deep Learning

Srivastava, Animesh; Shinde, Shantanu; Patel, Naeem; Despande, Siddhesh; Dalvi, Anuj; Tripathi, Shweta

doi:10.1109/ic-etite47903.2020.212

Cited by 12 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the development of related technologies in the field of artificial intelligence, in recent years, QG research has attracted wide attention from researchers. [20][21][22] Due to the complexity of Chinese grammar and syntax, there are relatively few research results related to the generation of Chinese problems. The rule-based model is mainly used to study Chinese QG.…”

Section: Related Workmentioning

confidence: 99%

Attention virtual adversarial based semi‐supervised question generation

Wang

Sun

et al. 2021

Concurrency and Computation

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%