论文标题
用于生成问答对的标签 - 序列学习
Tag-Set-Sequence Learning for Generating Question-Answer Pairs
论文作者
论文摘要
基于变压器的QG模型可以生成具有较高品质的问答对（QAPS），但也可能为某些文本产生愚蠢的问题。我们提出了一种称为标签序列学习来解决此问题的新方法，其中标签序列是捕获基础句子的句法和语义信息的一系列标签集，并且标签集由一个或多个语言特征标签组成，包括语义 - 语音 - 语音 - 词性标签，例如语音标签，部分命名，命名为“命名性”标签和sendertition-sectiment-sectiment-sectiment-sectiment-sectiment-nater-intermition-intermentiation-sendication-intermentiation-sendication-interigation-intimation-indictiation-indication-instsssssssssssssssssssssssssssssss。我们构建了一个名为TSS-Learner的系统，可以从给定的声明句子和相应的疑问句子中学习标签序列，并为后者提供答案。我们使用小型培训数据集训练英语的TSS学习模型，并表明它确实可以为基于变形金刚的模型差的某些文本生成足够的QAP。对TSS学习者对SAT练习阅读测试产生的QAP的人体评估令人鼓舞。
Transformer-based QG models can generate question-answer pairs (QAPs) with high qualities, but may also generate silly questions for certain texts. We present a new method called tag-set sequence learning to tackle this problem, where a tag-set sequence is a sequence of tag sets to capture the syntactic and semantic information of the underlying sentence, and a tag set consists of one or more language feature tags, including, for example, semantic-role-labeling, part-of-speech, named-entity-recognition, and sentiment-indication tags. We construct a system called TSS-Learner to learn tag-set sequences from given declarative sentences and the corresponding interrogative sentences, and derive answers to the latter. We train a TSS-Learner model for the English language using a small training dataset and show that it can indeed generate adequate QAPs for certain texts that transformer-based models do poorly. Human evaluation on the QAPs generated by TSS-Learner over SAT practice reading tests is encouraging.
