  Question generation is a challenging task which aims to ask a question based
on an answer and relevant context. The existing works suffer from the
mismatching between question type and answer, i.e. generating a question with
type $how$ while the answer is a personal name. We propose to automatically
predict the question type based on the input answer and context. Then, the
question type is fused into a seq2seq model to guide the question generation,
so as to deal with the mismatching problem. We achieve significant improvement
on the accuracy of question type prediction and finally obtain state-of-the-art
results for question generation on both SQuAD and MARCO datasets.
