An Answer Driven Model For Paragraph-level Question Generation

Answer-aware question generation aims to generate answerable questions from a given context and answers. Most of the current models are based on the attention-based sequence-to-sequence ($s$eq2seq) structure. However, these models do not make full use of the answer information, resulting in generating questions unrelated to the answer. We propose an answer driven model, which dynamically incorporates the interactive information between answer and previously generated words in the decoder to help the model decide which aspect of the question to focus on. Further, we use the answer distribution difference as a reward and use reinforcement learning to fine-tune the model. Experimental results show that our model performs better than the baseline models.