Joseph Marvin Imperial, Abdullah Barayan, Regina Stodden, Rodrigo Wilkens, Ricardo Muñoz Sánchez, Lingyun Gao, Melissa Torgbi, Dawn Knight, Gail Forey, Reka R. Jablonkai, Ekaterina Kochmar, Robert Joshua Reynolds, Eugénio Ribeiro, Horacio Saggion, Elena Volodina, Sowmya Vajjala, Thomas François, Fernando Alva-Manchego, Harish Tayyar Madabushi. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

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 newsdomain (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 bepublished parallel documents.Using this approach, we are dynamically increasing the webdomain 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.

In this system demonstration paper, we present an open-source web-based application with a responsive design for modular semantic frame annotation (SFA). Besides letting experienced and inexperienced users do suggestion-based and slightly-controlled annotations, the system keeps track of the time and changes during the annotation process and stores the users’ confidence with the current annotation. This collected metadata can be used to get insights regarding the difficulty of an annotation with the same type or frame or can be used as an input of an annotation cost measurement for an active learning algorithm. The tool was already used to build a manually annotated corpus with semantic frames and its arguments for task 2 of SemEval 2019 regarding unsupervised lexical frame induction (QasemiZadeh et al., 2019). Although English sentences from the Wall Street Journal corpus of the Penn Treebank were annotated for this task, it is also possible to use the proposed tool for the annotation of sentences in other languages.