Gap-fill Tests for Language Learners: Corpus-Driven Item Generation
Gap-fill Tests for Language Learners: Corpus-Driven Item Generation
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发表时间：
2010
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通讯作者：
Simon Smith;Avinesh P.V.S;A. Kilgarriff
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作者：
Simon Smith;Avinesh P.V.S;A. Kilgarriff
填空练习在语言教学中有着重要的作用。它们让学生证明他们理解上下文中的词汇，阻止记忆翻译。题目编写者要创造出好的测试题目是费时且困难的，即使这样，测试题目也会受到辛克莱对虚构例子的批评。我们提出了一个系统，TEDDCLOG，自动生成草案测试项目从语料库。TEDDCLOG将关键字（将形成练习正确答案的单词）作为输入。它发现干扰（替代，多选择问题的错误答案）从分布式词库，并确定一个搭配的关键，不发生干扰。接下来，它找到一个简单的语料库句子包含的关键字和搭配。然后，系统将句子和干扰词呈现给用户以供批准、修改或拒绝。该系统是使用API实现的草图引擎，一个领先的语料库查询系统。我们将TEDDCLOG与其他间隙填充生成系统进行比较，并对
Gap-fill exercises have an important role in language teaching. They allow students to demonstrate that they understand vocabulary in context, discouraging memorization of translations. It is timeconsuming and difficult for item writers to create good test items, and even then test items are open to Sinclair’s critique of invented examples. We present a system, TEDDCLOG, which automatically generates draft test items from a corpus. TEDDCLOG takes the key (the word which will form the correct answer to the exercise) as input. It finds distractors (the alternative, wrong answers for the multiplechoice question) from a distributional thesaurus, and identifies a collocate of the key that does not occur with the distractors. Next it finds a simple corpus sentence containing the key and collocate. The system then presents the sentences and distractors to the user for approval, modification or rejection. The system is implemented using the API to the Sketch Engine, a leading corpus query system. We compare TEDDCLOG with other gap-fill-generation systems, and offer a partial evaluation of the