Improving Reading Comprehension Question Generation with Data Augmentation and Overgenerate-and-rank

• URL: http://arxiv.org/abs/2306.08847v1
• Date: Thu, 15 Jun 2023 04:23:25 GMT
• Title: Improving Reading Comprehension Question Generation with Data Augmentation and Overgenerate-and-rank
• Authors: Nischal Ashok Kumar, Nigel Fernandez, Zichao Wang, Andrew Lan
• Abstract summary: 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. 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.
• Score: 3.854023945160742
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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