A computer-aided environment for generating multiple-choice test items
Natural Language Engineering
Automatic question generation for vocabulary assessment
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Dependency Analysis and CBR to Bridge the Generation Gap in Template-Based NLG
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
A case-based reasoning approach for speech corpus generation
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
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Automating the construction of multiple-choice questions (MCQs) is a challenge that has attracted the interest of artificial intelligence researchers for many years. We present a case-based reasoning (CBR) approach to this problem in which MCQs are automatically generated from cases describing events or experiences of interest (e.g., historical events, movie releases, sports events) in a given domain. Measures of interestingness and similarity are used in our approach to guide the retrieval of cases and case features from which questions, distractors, and hints for the user are generated in natural language. We also highlight a potential problem that may occur when similarity is used to select distractors for the correct answer in certain types of MCQ. Finally, we demonstrate and evaluate our approach in an intelligent system for automating the design of MCQ quizzes called AutoMCQ.