Difficulty Controllable Question Generation for Reading Comprehension
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Question generation aims to generate natural language questions from a range of data sources such as free text and image. In this paper, we investigate the difficulty levels of questions, and propose a new task called Difficulty Controllable Question Generation (Dico-QG). Taking as input a reading comprehension paragraph and some text fragments (i.e. answers) in the paragraph that we want to ask about, a Dico-QG method needs to generate questions each of which has a given text fragment as its answer and is associated with a difficulty label. To solve this task, we proposed a two-step approach. The first step estimates what difficulty level of question could be generated for a given answer. After that, in the generation step, the estimated difficulty is employed together with other information as input to generate a question. For evaluation, we prepared the first dataset of reading comprehension questions with difficulty labels. The results show that our approach not only generates questions of better quality under the metrics like BLEU, but also has the capability of difficulty awareness to generate questions complying with the difficulty label.
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