Improving Reading Comprehension Question Generation with Data
  Augmentation and Overgenerate-and-rank
Nischal Ashok Kumar Nigel Fernandez Zichao Wang Andrew Lan
Reading comprehension is a crucial skill in many aspects of education,
including language learning, cognitive development, and fostering early
literacy skills in children. Automated answer-aware reading comprehension
question generation has significant potential to scale up learner support in
educational activities. One key technical challenge in this setting is that
there can be multiple questions, sometimes very different from each other, with
the same answer; a trained question generation method may not necessarily know
which question human educators would prefer. To address this challenge, we
propose 1) a data augmentation method that enriches the training dataset with
diverse questions given the same context and answer and 2) an
overgenerate-and-rank method to select the best question from a pool of
candidates. We evaluate our method on the FairytaleQA dataset, showing a 5%
absolute improvement in ROUGE-L over the best existing method. We also
demonstrate the effectiveness of our method in generating harder, "implicit"
questions, where the answers are not contained in the context as text spans.