Diversify Question Generation based on Diffusion Model in Dialogue

抄録

Automatic Question Generation (AQG) aims to generate natural and relevant questions based on a given context and optional answers. It is a significant and challenging task in the field of natural language processing. However, existing AQG models often produce a single type of question with repetitive content, which hinders the diversity of the generated questions. In this paper, we introduce a Diversify Question Generation model based on the Diffusion Model (DQG-DM). Our model effectively incorporates latent variables and fine-grained question types to ensure both the relevance and diversity of the generated questions. Experiments conducted on two benchmark datasets demonstrate that our proposed model outperforms the state-of-the-art results.