While exam-style questions are a fundamental educational tool serving a variety of purposes, manual construction of questions is a complex process that requires training, experience and resources. To reduce the expenses associated with the manual construction of questions and to satisfy the need for a continuous supply of new questions, automatic question generation (QG) techniques can be utilized. However, compared to automatic question answering (QA), QG is a more challenging task. In this work, we fine-tune a multilingual T5 (mT5) transformer in a multi-task setting for QA, QG and answer extraction tasks using a Turkish QA dataset. To the best of our knowledge, this is the first academic work that attempts to perform automated text-to-text question generation from Turkish texts. Evaluation results show that the proposed multi-task setting achieves state-of-the-art Turkish question answering and question generation performance over TQuADv1, TQuADv2 datasets and XQuAD Turkish split. The source code and pre-trained models are available at https://github.com/obss/turkish-question-generation.

翻译：虽然考试式问题是一个基本的教育工具,可以满足各种目的,但人工解决问题是一个复杂的过程,需要培训、经验和资源。为了减少人工解决问题的费用,满足持续提供新问题的需求,可以使用自动生成问题的技术。但是,与自动回答问题(QA)相比,QG是一项更具有挑战性的任务。在这项工作中,我们在QA、QG的多任务设置中微调多语种T5变压器(mT5),并使用土耳其QA数据集回答提取任务。据我们所知,这是首次尝试从土耳其文本中自动生成文本到文本的问题。评价结果显示,拟议的多任务设置实现了TQuADv1、TQADv2数据集和XQAD土耳其分解式的土耳其问题解答和问题生成状态。源代码和预培训模式见https://github.com/obs/turkish-Misdelage。