Generating Quizzes to Support Training on Quality Management and Assurance in Space Science and Engineering
This work addresses the need for automated quiz generation to support training in quality assurance for space agencies, which is incremental as it applies existing NLP methods to a specific domain.
The paper tackled the problem of evaluating training effectiveness in quality management for space science by developing a system that generates quizzes from documents, using T5 and BART for question generation and RoBERTa for answer extraction to verify suitability.
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.