Mask and Cloze: Automatic Open Cloze Question Generation using a Masked Language Model
Shoya Matsumori, Kohei Okuoka, Ryoichi Shibata, Minami Inoue, Yosuke, Fukuchi, Michita Imai

TL;DR
This paper introduces CLOZER, an automatic open cloze question generator using a masked language model, which effectively creates questions that accept only the correct answer, easing teachers' workload and aiding language learning.
Contribution
The paper presents CLOZER, a novel system that automatically generates open cloze questions with high accuracy, outperforming average non-native teachers and demonstrating practical benefits in educational settings.
Findings
CLOZER successfully generates questions accepting only the ground truth answer.
CLOZER outperforms average non-native English teachers in question quality.
Students find CLOZER useful for language learning.
Abstract
Open cloze questions have been attracting attention for both measuring the ability and facilitating the learning of L2 English learners. In spite of its benefits, the open cloze test has been introduced only sporadically on the educational front, largely because it is burdensome for teachers to manually create the questions. Unlike the more commonly used multiple choice questions (MCQ), open cloze questions are in free form and thus teachers have to ensure that only a ground truth answer and no additional words will be accepted in the blank. To help ease this burden, we developed CLOZER, an automatic open cloze question generator. In this work, we evaluate CLOZER through quantitative experiments on 1,600 answers and show statistically that it can successfully generate open cloze questions that only accept the ground truth answer. A comparative experiment with human-generated questions…
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Taxonomy
TopicsTopic Modeling · Expert finding and Q&A systems · Educational Technology and Assessment
