Question-type Driven Question Generation
Wenjie Zhou, Minghua Zhang, Yunfang Wu

TL;DR
This paper introduces a method that predicts question types from answers and context, then incorporates this into a seq2seq model to improve question generation accuracy, achieving state-of-the-art results on SQuAD and MARCO datasets.
Contribution
It presents a novel approach that predicts question types and integrates them into question generation, addressing the mismatch issue in prior methods.
Findings
Significant improvement in question type prediction accuracy.
State-of-the-art question generation results on SQuAD.
Enhanced alignment between question types and answers.
Abstract
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 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.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Multimodal Machine Learning Applications
MethodsSigmoid Activation · Tanh Activation · Long Short-Term Memory · Sequence to Sequence
