Generating Answer Candidates for Quizzes and Answer-Aware Question Generators
This addresses the need for automated tools to assist instructors in creating educational quizzes, though it is incremental as it builds on existing question generation methods.
The paper tackles the problem of generating answer candidates for open-ended quiz questions, which is often overlooked in automatic question generation research, and shows that their proposed model outperforms several baselines.
In education, open-ended quiz questions have become an important tool for assessing the knowledge of students. Yet, manually preparing such questions is a tedious task, and thus automatic question generation has been proposed as a possible alternative. So far, the vast majority of research has focused on generating the question text, relying on question answering datasets with readily picked answers, and the problem of how to come up with answer candidates in the first place has been largely ignored. Here, we aim to bridge this gap. In particular, we propose a model that can generate a specified number of answer candidates for a given passage of text, which can then be used by instructors to write questions manually or can be passed as an input to automatic answer-aware question generators. Our experiments show that our proposed answer candidate generation model outperforms several baselines.