Call for paper

We invite submissions on a broad range of topics related to German text simplification and complexity assessment, including but not limited to the following:

• German Text Simplification:
  Methods for lexical, syntactic, and discourse-level simplification, including rule-based approaches, neural models, LLMs, and hybrid techniques. Evaluation frameworks for measuring simplification quality and linguistic fidelity.
• Readability Assessment:
  Computational readability metrics, corpus-based and machine-learning-driven models, and domain-specific readability considerations (e.g., medical, legal, and news texts).
• Resources & Approaches for Leichte Sprache:
  Parallel corpora with samples in Leichte Sprache, automated approaches to adhere to the rules in DIN SPEC 33429, participatory research projects including the target group.
• The Role of Large Language Models (LLMs):
  The risks and biases associated with LLM-driven simplification. Exploring the application of large language models in text simplification, readability prediction, and controlled text generation.
• Resources & Benchmarks:
  The creation and standardization of German-language datasets for simplification and readability assessment, including human-annotated corpora, automatic scoring methods, and domain-specific benchmarks.
• Evaluation & Human-Centered Assessment:
  Beyond automatic metrics (e.g., Flesch Reading Ease, perplexity), the workshop will explore human-centered evaluation strategies, incorporating usability studies, eye-tracking, and cognitive load assessment.
• Applications & Real-World Impact:
  Integration of simplification and readability models into NLP applications, such as assistive tools, automatic text adaptation for different reading levels, and AI-driven tutoring systems.
• Cross-Linguistic & Multilingual Perspectives:
  Comparisons between German and other languages, multilingual simplification models, and cross-lingual readability assessment approaches.