Difficulty Controllable Question Generation for Reading Comprehension.
Year: 2018
Type: preprint
Abstract: Question generation aims to generate natural language questions from a rangeof 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.
Source: arXiv (Cornell University)
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2812757605