Generating Multiple-Choice Reading Comprehension Questions using Paraphrase
Date Issued
2014
Date
2014
Author(s)
Tseng, Ya-Min
Abstract
As online English learning environment becomes more and more ubiquitous, English as a Foreign Language (EFL) learners have more choices to learning English. There is thus an increasing demand for automatic assessment tools that help self-motivated learners evaluate their understanding and comprehension. Existing question generation systems, however, focus on the sentence-to-question surface transformation and the questions could be simply answered by word matching, even without good comprehension. We propose a novel approach to generating multiple-choice reading comprehension questions by combining paraphrase generation with question generation. To achieve this, we build a system that consists of three components. In the Choice Generation System, transformation rules are designed to ensure that every generated statement is bound up with a specific testing purpose. Discourse relation recognition and the concept of paraphrasing are also introduced into the system, enriching the choice candidates. The Paraphrase Generation System then moves on to enlarge the superficial difference by paraphrasing lexically, syntactically and referentially. We adopt QG-specific paraphrase resource, nominal coreference, into the system to capture article-wide coreferential relations. Finally, the Acceptability Ranker is trained based on useful features that have been seen in paraphrase generation and question generation to rank paraphrases by their acceptability as question choices. In the final evaluation, although there is a slight decrease in the scores of grammaticality, make-sense and overall quality, our results outperform the baseline system in the challenging score and have a significantly smaller percentage of statements that are identical to the sources sentences.
Subjects
自動出題
自動評量
閱讀理解
線上學習
選擇題
改寫生成
語句關係
Type
thesis
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