In this paper, we tackle the task of distractor generation (DG) for multiple-choice questions. Our study introduces two key designs. First, we propose the concept of retrieval augmented pretraining, which involves refining the language model pretraining to align it more closely with the downstream task of DG. Second, we explore the integration of knowledge graphs and language models to further enhance the performance of DG. Our study unveils promising directions for further development in DG by showcasing the efficacy of knowledge augmentation and task-specific pretraining. These findings demonstrate the potential for leveraging both strategies to enhance the quality and performance of DG systems.

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).