This paper examines the current state-of-the-art of German text
simplification, focusing on parallel and monolingual German corpora. It reviews
neural language models for simplifying German texts and assesses their
suitability for legal texts and accessibility requirements. Our findings
highlight the need for additional training data and more appropriate approaches
that consider the specific linguistic characteristics of German, as well as the
importance of the needs and preferences of target groups with cognitive or
language impairments. The authors launched the interdisciplinary OPEN-LS
project in April 2023 to address these research gaps. The project aims to
develop a framework for text formats tailored to individuals with low literacy
levels, integrate legal texts, and enhance comprehensibility for those with
linguistic or cognitive impairments. It will also explore cost-effective ways
to enhance the data with audience-specific illustrations using image-generating
AI.
For more and up-to-date information, please visit our project homepage
this https URL

德文文本简化的当前状况，重点研究平行和单语德语语料库。评估神经语言模型在简化德语文本方面的适用性，特别是在法律文本和无障碍要求方面。研究结果强调了需要更多的训练数据和更恰当的方法，考虑到德语的特定语言特征，以及针对认知或语言障碍目标群体的需求和偏好的重要性。为了解决这些研究空白，作者们于 2023 年 4 月启动了跨学科的 OPEN-LS 项目。该项目旨在开发适合低识字水平个体使用的文本格式框架，整合法律文本，并提高对语言或认知障碍者的可理解性。此外，该项目还将探索使用生成图像的人工智能以增强面向特定受众的插图数据的成本效益方法。欲了解更多和最新信息，请访问我们的项目主页：https://URL-Link