Year: 2025
Type: conference-paper
Abstract: Question Generation (QG), the task of automatically generating questions from a source input, has seen significant progress in recent years.Difficulty-controllable QG (DCQG) enables control over the difficulty level of generated questions while considering the learner's ability.Additionally, narrative-controllable QG (NCQG) allows control over the narrative aspects embedded in the questions.However, research in QG lacks a focus on combining these two types of control, which is important for generating questions tailored to educational purposes.To address this gap, we propose a strategy for Joint Narrative and Difficulty Control, enabling simultaneous control over these two attributes in the generation of reading comprehension questions.Our evaluation provides preliminary evidence that this approach is feasible, though it is not effective across all instances.Our findings highlight the conditions under which the strategy performs well and discuss the trade-offs associated with its application.
Authors: Bernardo Leite, Henrique Lopes Cardoso
Institution: Universidade do Porto
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W4412889191