Distractor Generation Using Generative and Discriminative Capabilities of Transformer-based Models (2024.lrec-main)
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| Challenge: | Multiple Choice Questions (MCQs) are used to test language learners' comprehension and knowledge. |
| Approach: | They propose an automatic distractor generation approach which generates correct and incorrect answer options and then discriminates potential correct options from distractors. |
| Outcome: | The proposed approach outperforms previous models on multiple choice questions and reading comprehension questions. |
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| Challenge: | Multiple-choice questions (MCQs) are critical for identifying misconceptions and gaps in knowledge and accurately assessing students' understanding. |
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| Challenge: | a new variational approach to distractors in multiple-choice questions is needed . high-quality distractors are crucial to the assessment and pedagogical value of MCQs . a variational method that learns the error behind distractors is more effective . |
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| Challenge: | Existing distractor generation methods are far from practical, and there are still room for improvement. |
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