@inproceedings{wang-etal-2023-distractor,
    title = "Distractor Generation based on {T}ext2{T}ext Language Models with Pseudo {K}ullback-{L}eibler Divergence Regulation",
    author = "Wang, Hui-Juan  and
      Hsieh, Kai-Yu  and
      Yu, Han-Cheng  and
      Tsou, Jui-Ching  and
      Shih, Yu An  and
      Huang, Chen-Hua  and
      Fan, Yao-Chung",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.790",
    doi = "10.18653/v1/2023.findings-acl.790",
    pages = "12477--12491",
    abstract = "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).",
}

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).
Wang, Hui-Juan Hsieh, Kai-Yu Yu, Han-Cheng Tsou, Jui-Ching Shih, Yu An Huang, Chen-Hua Fan, Yao-Chung
Distractor Generation based on Text2Text Language Models with Pseudo Kullback-Leibler Divergence Regulation