Reading comprehension tasks are commonly used for developing students’ reading ability. In order to adaptively recommend reading comprehension materials to students engaged in computerized testing, the information in an item bank (a collection of test items stored in a dataset) must be effectively indexed. Familiarity with the topics present in the documents influences students’ reading performance. As different question types require different skills, we tag documents with topics and questions with their corresponding types to measure the students’ abilities and subsequently recommend relevant materials to them. However, automatic tagging has not been extensively studied in this field. In this article, we propose a document extraction attention network (DEAN) to accomplish the two aforementioned tasks. For topic tagging, DEAN utilizes questions to increase the sample size of documents implicitly through multitask learning. For type tagging, DEAN leverages the information gathered from documents, which aids in the task of prediction. Experiments demonstrate the effectiveness of our mutual use of information obtained from documents and questions. Results indicate that DEAN outperforms commonly used text classification methods when tested on a reading comprehension dataset.

中文翻译：

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使用文档提取注意网络标记阅读理解材料

阅读理解任务通常用于培养学生的阅读能力。为了适应性地向参加计算机化测试的学生推荐阅读理解材料，必须有效地索引项目库（存储在数据集中的测试项目的集合）中的信息。熟悉文档中的主题会影响学生的阅读表现。由于不同的问题类型要求不同的技能，因此我们在文档中标记主题和问题以及相应的类型，以衡量学生的能力，并随后向他们推荐相关材料。但是，自动标记在这一领域尚未得到广泛研究。在本文中，我们提出了一个文档提取注意网络（DEAN）以完成上述两个任务。对于主题标记，DEAN通过多任务学习利用问题来隐式地增加文档的样本量。对于类型标记，DEAN利用从文档收集的信息，这有助于进行预测。实验证明了我们相互使用从文档和问题中获得的信息的有效性。结果表明，在阅读理解数据集上进行测试时，DEAN的性能优于常用的文本分类方法。