This study sets out to investigate the feasibility of using ChatGPT to
translate citizen-oriented administrative texts into German Easy Language, a
simplified, controlled language variety that is adapted to the needs of people
with reading impairments. We use ChatGPT to translate selected texts from
websites of German public authorities using two strategies, i.e. linguistic and
holistic. We analyse the quality of the generated texts based on different
criteria, such as correctness, readability, and syntactic complexity. The
results indicated that the generated texts are easier than the standard texts,
but that they still do not fully meet the established Easy Language standards.
Additionally, the content is not always rendered correctly.

研究使用 ChatGPT 将市民导向的行政文本翻译成了德语简化易语言，使用语言和整体两种策略进行翻译，并对生成的文本进行质量分析，发现生成的文本比标准文本更易于理解，但仍未完全符合规定的简化易语言标准，而且内容有时不正确。

使用 ChatGPT 作为 Easy Language 翻译的 CAT 工具

Using ChatGPT as a CAT tool in Easy Language translation

Automatic text simplification systems help to reduce textual information
barriers on the internet. However, for languages other than English, only few
parallel data to train these systems exists. We propose a two-step approach to
overcome this data scarcity issue. First, we fine-tuned language models on a
corpus of German Easy Language, a specific style of German. Then, we used these
models as decoders in a sequence-to-sequence simplification task. We show that
the language models adapt to the style characteristics of Easy Language and
output more accessible texts. Moreover, with the style-specific pre-training,
we reduced the number of trainable parameters in text simplification models.
Hence, less parallel data is sufficient for training. Our results indicate that
pre-training on unaligned data can reduce the required parallel data while
improving the performance on downstream tasks.

本文提出了一种通过在德国简单语言语料库上微调语言模型并将其作为序列到序列简化任务的解码器来克服数据稀缺性问题的两步方法，结果表明，在不同于英语的语言上进行无对齐预训练可以降低所需的平行数据量，同时提高下游任务的性能。