We present a system called TP3 to perform a downstream task of transformers on generating question-answer pairs (QAPs) from a given article. TP3 first finetunes pretrained transformers on QAP datasets, then uses a preprocessing pipeline to select appropriate answers, feeds the relevant sentences and the answer to the finetuned transformer to generate candidate QAPs, and finally uses a postprocessing pipeline to filter inadequate QAPs. In particular, using pretrained T5 models as transformers and the SQuAD dataset as the finetruning dataset, we show that TP3 generates satisfactory number of QAPs with high qualities on the Gaokao-EN dataset.

翻译：我们提出了一个称为TP3的系统,用于执行变压器下游任务,从特定文章中生成问答配对。 TP3首先在QAP数据集上对预先训练的变压器进行微调,然后使用预处理管道来选择适当的答案,为相关句子和经过微调的变压器的答案提供材料,以产生候选QAP,最后使用后处理管道过滤不充分的QAP。 特别是,使用预先训练过的T5模型作为变压器,以及SQuAD数据集作为微调数据集,我们显示TP3生成了数量令人满意的高卡奥-EN数据集高质量QAP。