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.

本研究旨在使用自然语言生成技术快速生成英语语言学习应用的内容，并控制生成的结果以满足相关要求。根据语言学习水平及语法结构等因素，我们尝试使用预训练深度模型进行控制，评价结果表明，我们可以控制实现多样化、定制化内容的同时，实现较高的语法质量。