Text simplification is an intralingual translation task in which documents, or sentences of a complex source text are simplified for a target audience. The success of automatic text simplification systems is highly dependent on the quality of parallel data used for training and evaluation. To advance sentence simplification and document simplification in German, this paper presents DEplain, a new dataset of parallel, professionally written and manually aligned simplifications in plain German ("plain DE" or in German: "Einfache Sprache"). DEplain consists of a news domain (approx. 500 document pairs, approx. 13k sentence pairs) and a web-domain corpus (approx. 150 aligned documents, approx. 2k aligned sentence pairs). In addition, we are building a web harvester and experimenting with automatic alignment methods to facilitate the integration of non-aligned and to be published parallel documents. Using this approach, we are dynamically increasing the web domain corpus, so it is currently extended to approx. 750 document pairs and approx. 3.5k aligned sentence pairs. We show that using DEplain to train a transformer-based seq2seq text simplification model can achieve promising results. We make available the corpus, the adapted alignment methods for German, the web harvester and the trained models here: https://github.com/rstodden/DEPlain.

翻译：文本简化是一项语内翻译任务，旨在将复杂源文本中的文档或句子简化为目标读者易于理解的形式。自动文本简化系统的成功高度依赖于训练和评估所用平行数据的质量。为推进德语句子简化和文档简化研究，本文提出了DEplain数据集——一个由专业撰写、人工对齐的德语简明文本（即"Einfache Sprache"）构成的平行语料库。该数据集包含新闻领域（约500对文档、约1.3万对句子）和网络领域（约150对文档、约2千对句子）两大部分。此外，我们正在开发网络采集器并实验自动对齐方法，以促进非对齐及待发布平行文档的整合。通过该方案，我们动态扩充网络领域语料库，目前规模已扩展至约750对文档和约3.5千对句子。实验表明，基于DEplain训练的Transformer序列到序列文本简化模型能取得显著效果。我们已在如下地址公开该语料库、针对德语适配的对齐方法、网络采集器及训练模型：https://github.com/rstodden/DEPlain。