Hui-Juan Wang


2023

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Distractor Generation based on Text2Text Language Models with Pseudo Kullback-Leibler Divergence Regulation
Hui-Juan Wang | Kai-Yu Hsieh | Han-Cheng Yu | Jui-Ching Tsou | Yu An Shih | Chen-Hua Huang | Yao-Chung Fan
Findings of the Association for Computational Linguistics: ACL 2023

In this paper, we address the task of cloze-style multiple choice question (MCQs) distractor generation. Our study is featured by the following designs. First, we propose to formulate the cloze distractor generation as a Text2Text task. Second, we propose pseudo Kullback-Leibler Divergence for regulating the generation to consider the item discrimination index in education evaluation. Third, we explore the candidate augmentation strategy and multi-tasking training with cloze-related tasks to further boost the generation performance. Through experiments with benchmarking datasets, our best perfomring model advances the state-of-the-art result from 10.81 to 22.00 (p@1 score).