分析多项选择阅读与听力理解测试
Analyzing Multiple-Choice Reading and Listening Comprehension Tests
AI总结：
本文研究了多项选择阅读与听力理解测试中上下文段落的必要阅读量，发现自动系统在部分或无上下文时仍优于随机水平，为内容创作者自动权衡问题所需的理解能力与世界知识提供了方法。
摘要：
多项选择阅读与听力理解测试是语言评估的重要组成部分。标准化教育测试的内容创作者需要精心编制问题，以评估参加测试考生的理解能力。然而，近期研究表明，在一般多项选择阅读理解数据集中，大量问题无需理解即可作答，而是通过利用世界知识来解答。本研究调查了在基于对话转录文本的多项选择阅读和听力理解测试中，需要阅读多少上下文段落才能得出正确答案。我们发现，自动阅读理解系统在部分访问甚至无法访问上下文段落的情况下，其表现仍显著优于随机水平。这些发现为内容创作者提供了一种方法，使其能够自动衡量所提出问题在所需的理解能力与世界知识之间的权衡。
英文摘要：
Multiple-choice reading and listening comprehension tests are an important part of language assessment. Content creators for standard educational tests need to carefully curate questions that assess the comprehension abilities of candidates taking the tests. However, recent work has shown that a large number of questions in general multiple-choice reading comprehension datasets can be answered without comprehension, by leveraging world knowledge instead. This work investigates how much of a contextual passage needs to be read in multiple-choice reading based on conversation transcriptions and listening comprehension tests to be able to work out the correct answer. We find that automated reading comprehension systems can perform significantly better than random with partial or even no access to the context passage. These findings offer an approach for content creators to automatically capture the trade-off between comprehension and world knowledge required for their proposed questions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
