Neural Question Generation with Answer Pivot

Neural question generation (NQG) is the task of generating questions from the given context with deep neural networks. Previous answer-aware NQG methods suffer from the problem that the generated answers are focusing on entity and most of the questions are trivial to be answered. The answeragnostic NQG methods reduce the bias towards named entities and increasing the model’s degrees of freedom, but sometimes result in generating unanswerable questions which are not valuable for the subsequent machine reading comprehension system. In this paper, we treat the answers as the hidden pivot for question generation and combine the question generation and answer selection process in a joint model. We achieve the state-of-the-art result on the SQuAD dataset according to automatic metric and human evaluation.

Authors

• Bingning Wang
• Xiaochuan Wang
• Ting Tao
• Qi Zhang School of Computer Science, Fudan University, Shanghai, China Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China
• Jingfang Xu

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