Automatic Generation System of Multiple-Choice Cloze Questions and its Evaluation

Abstract

Since English expressions vary according to the genres, it is important for students to study questions that are generated from sentences of the target genre. Although various questions are prepared, it is still not enough to satisfy various genres which students want to learn. On the other hand, when producing English questions, sufficient grammatical knowledge and vocabulary are needed, so it is difficult for non-expert to prepare English questions by themselves. In this paper, we propose an automatic generation system of multiple-choice cloze questions from English texts. Empirical knowledge is necessary to produce appropriate questions, so machine learning is introduced to acquire knowledge from existing questions. To generate the questions from texts automatically, the system (1) extracts appropriate sentences for questions from texts based on Preference Learning, (2) estimates a blank part based on Conditional Random Field, and (3) generates distracters based on statistical patterns of existing questions. Experimental results show our method is workable for selecting appropriate sentences and blank part. Moreover, our method is appropriate to generate the available distracters, especially for the sentence that does not contain the proper noun.

https://doi.org/10.34105/j.kmel.2010.02.016