Question-type Driven Question Generation
This work addresses a specific bottleneck in question generation for NLP applications, but it is incremental as it builds on existing seq2seq methods.
The paper tackled the problem of mismatching between question type and answer in question generation by predicting the question type from the input and integrating it into a seq2seq model, achieving state-of-the-art results on SQuAD and MARCO datasets.
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.