Difficulty Controllable Generation of Reading Comprehension Questions
This addresses the need for customizable question generation in educational and assessment tools, though it is incremental as it builds on existing question generation methods by adding difficulty control.
The paper tackles the problem of generating reading comprehension questions with controllable difficulty levels by proposing a new setting called Difficulty-controllable Question Generation (DQG), and results show that their framework produces questions with better quality metrics like BLEU and adherence to specified difficulty labels.
We investigate the difficulty levels of questions in reading comprehension datasets such as SQuAD, and propose a new question generation setting, named Difficulty-controllable Question Generation (DQG). Taking as input a sentence in the reading comprehension paragraph and some of its text fragments (i.e., answers) that we want to ask questions about, a DQG method needs to generate questions each of which has a given text fragment as its answer, and meanwhile the generation is under the control of specified difficulty labels---the output questions should satisfy the specified difficulty as much as possible. To solve this task, we propose an end-to-end framework to generate questions of designated difficulty levels by exploring a few important intuitions. For evaluation, we prepared the first dataset of reading comprehension questions with difficulty labels. The results show that the question generated by our framework not only have better quality under the metrics like BLEU, but also comply with the specified difficulty labels.