This work aims to employ natural language generation (NLG) to rapidly generate items for English language learning applications: this requires both language models capable of generating fluent, high-quality English, and to control the output of the generation to match the requirements of the relevant items. We experiment with deep pretrained models for this task, developing novel methods for controlling items for factors relevant in language learning: diverse sentences for different proficiency levels and argument structure to test grammar. Human evaluation demonstrates high grammatically scores for all models (3.4 and above out of 4), and higher length (24%) and complexity (9%) over the baseline for the advanced proficiency model. Our results show that we can achieve strong performance while adding additional control to ensure diverse, tailored content for individual users.

翻译：这项工作旨在利用自然语言生成(NLG)迅速生成英语学习应用项目:这既需要能够产生流利、高质量英语的语言模式,也需要控制生成的输出,以符合相关项目的要求。我们试验了这一任务的深层预先培训模式,开发了用于控制与语言学习相关的要素的物项的新颖方法:不同熟练程度的不同句子和用于测试语法的论证结构。人文评估显示,所有模型(4个中3.4个和以上)的语法得分都很高,高级熟练程度模型基线的长度(24%)和复杂性(9%)更高。我们的结果表明,我们可以取得很强的绩效,同时增加更多的控制,确保针对个人用户的多样化和量身定制的内容。