Given a passage, a question, and an answer phrase, the goal of distractor generation (DG) is to gener- ate context-related wrong options (i.e., distractor) for multiple-choice questions (MCQ).
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We investigate the task of distractor generation for multiple choice reading comprehension questions from examinations.
We investigate how machine learning models, specifically ranking models, can be used to select useful distractors for multiple choice questions.
In this paper, we investigate the following two limitations for the existing distractor generation (DG) methods.
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