In this paper, we propose a novel configurable framework to automatically generate distractive choices for open-domain cloze-style multiple-choice questions. The incorporates general-purpose knowledge base effectively create small distractor candidate set, and feature-rich learning-to-rank model select distractors that are both plausible reliable. Experimental results on new dataset across four...

Multiple-choice cloze items constitute a prominent tool for assessing students’ competency in using the vocabulary of a language correctly. Without a proper estimation of students’ competency in using vocabulary, it will be hard for a computer-assisted language learning system to provide course material tailored to each individual student’s needs. Computer-assisted item generation allows the cr...

1 We report experience in applying techniques for natural language processing to algorithmically generating test items for both reading and listening cloze items. We propose a word sense disambiguationbased method for locating sentences in which designated words carry specific senses, and apply a collocation-based method for selecting distractors that are necessary for multiple-choice cloze ite...

We describe the automated generation and use of 69,326 comprehension cloze questions and 5,668 vocabulary matching questions in the 2001-2002 version of Project LISTEN’s Reading Tutor used by 364 students in grades 1-9 at seven schools. To validate our methods, we used students’ performance on these multiple-choice questions to predict their scores on the Woodcock Reading Mastery Test. A model ...

1 We report experience in applying techniques for natural language processing to algorithmically generating test items for both reading and listening cloze items. We propose a word sense disambiguationbased method for locating sentences in which designated words carry specific senses, and apply a collocation-based method for selecting distractors that are necessary for multiple-choice cloze ite...