Affix-based Distractor Generation for Tamil Multiple Choice Questions using Neural Word Embedding

Abstract

Assessment plays an important role in learning and Multiple Choice Questions (MCQs) are quite popular in large-scale evaluations. Technology enabled learning necessitates a smart assessment. Therefore, automatic MCQ generation became increasingly popular in the last two decades. Despite a large amount of research effort, system generated MCQs are not useful in real educational applications. This is because of the inability to produce the diverse and human alike distractors. Distractors are the wrong choices given along with the correct answer (key) to confuse the examinee. Especially, in educational domain (grammar learning) the MCQs deal with affix-based or morphologically transformed distractors. In this paper, we present a method for automatic generation of affix-based distractors for fill-in-the-blanks for learning Tamil Vocabulary. Affix-based distractor generation relies on certain regularities manifest in high dimensional spaces. We investigate the quality of distractors generated by a number of criteria, including Part-Of-Speech, difficulty level, spelling, word co-occurrence, semantic similarity and affixation. We evaluated our proposed method in grammar based Multiple Choice Questions (MCQs) dataset. The result shows that affix-based distractors, yield significantly more plausible outcomes in certain grammar based questions.