论文标题
元序列学习，用于产生足够的问答对
Meta Sequence Learning for Generating Adequate Question-Answer Pairs
论文作者
论文摘要
创建多项选择问题来评估给定文章的阅读理解，涉及在文档的要点上生成问题 - 答案对（QAPS）。我们提出了一个学习方案，以通过句子的元序列表示足够的QAP。元序列是包含语义和句法标签的矢量序列。特别是，我们设计了一个名为Metaqa的方案，以从训练数据中学习元序列，以形成对声明句子（MD）的元序列对成对的，并相应的疑问句（MIS）。在给定的声明性句子上，经过训练的元数据模型将其转换为元序列，找到匹配的MD，并使用相应的MIS和输入句子生成QAPS。我们使用语义角色标签，词性标签和指定性识别来实现英语的元数据，并显示在小型数据集上训练的元素，Metaqa在官方的SAT练习练习测试中有效地生成了大量的语法和语义上正确的QAP，具有超过97 \％的准确性。
Creating multiple-choice questions to assess reading comprehension of a given article involves generating question-answer pairs (QAPs) on the main points of the document. We present a learning scheme to generate adequate QAPs via meta-sequence representations of sentences. A meta sequence is a sequence of vectors comprising semantic and syntactic tags. In particular, we devise a scheme called MetaQA to learn meta sequences from training data to form pairs of a meta sequence for a declarative sentence (MD) and a corresponding interrogative sentence (MIs). On a given declarative sentence, a trained MetaQA model converts it to a meta sequence, finds a matched MD, and uses the corresponding MIs and the input sentence to generate QAPs. We implement MetaQA for the English language using semantic-role labeling, part-of-speech tagging, and named-entity recognition, and show that trained on a small dataset, MetaQA generates efficiently over the official SAT practice reading tests a large number of syntactically and semantically correct QAPs with over 97\% accuracy.
