Quality management and assurance is key for space agencies to guarantee the success of space missions, which are high-risk and extremely costly. In this paper, we present a system to generate quizzes, a common resource to evaluate the effectiveness of training sessions, from documents about quality assurance procedures in the Space domain. Our system leverages state of the art auto-regressive models like T5 and BART to generate questions, and a RoBERTa model to extract answers for such questions, thus verifying their suitability.
@article{arxiv.2210.03427,
title = {Generating Quizzes to Support Training on Quality Management and Assurance in Space Science and Engineering},
author = {Andrés García-Silva and Cristian Berrío and José Manuel Gómez-Pérez},
journal= {arXiv preprint arXiv:2210.03427},
year = {2022}
}
Comments
In Proceedings of the 15th International Natural Language Generation Conference (INLG 2022)