Call for Papers

Important Dates

All deadlines are 11:59PM UTC-12:00 (“anywhere on Earth”).

  • Submission deadline: 26 August 2025
  • Notification of acceptance: 30 September 2025
  • Camera-ready papers due: 7 October 2025 (TBC)
  • Workshop: 5-9 November 2025

The organisers are pleased to present the fourth instalment of the annual workshop on Text Simplification, Accessibility and Readability (TSAR 2025), colocated with EMNLP 2025 in Suzhou, China. TSAR aims to provide a cohesive environment to draw members of the computational linguistics, natural language processing and artificial intelligence communities working on the use of automated techniques to make language accessible for all. Previous editions of the workshop have been held at EMNLP (2022, 2024) and RANLP (2023) with significant engagement and participation driving research and building community in the fields of Text Simplification, Accessibility and Readability.

Research in automatic text simplification (TS) has often focused on proposing the use of methods derived from the deep learning paradigm (Martin et al., 2020; Maddela et al., 2021; Sheang and Saggion, 2021). Recently, work in text simplification has leveraged the new era of foundational large language models through fine-tuning and prompt-engineering to produce simplifications (Kew et al. 2023; Cripwell et al. 2023; Farajidizaji et al. 2024).

However, there are many important aspects in automatic TS that require the attention of our community: the design of appropriate evaluation metrics (Štajner et al., 2022), the development of context-aware simplification solutions (Yimam et al., 2018; Shardlow et al., 2022a; Saggion et al., 2022), the creation of appropriate language resources to support research and evaluation (Maddela and Xu, 2018; Shardlow et al., 2020; Ferres and Saggion, 2022; North et al., 2022), the deployment of simplification in real environments for real users (Lee and Yeung, 2018; Alonzo et al., 2022), the study of discourse factors in text simplification (Zhong et al., 2019), the identification of factors affecting the readability of a text (Shardlow et al., 2022b), among others. To overcome those issues, there is a need for collaboration of CL/NLP researchers, machine learning and deep learning researchers, UI/UX and Accessibility professionals, as well as public organisations representatives (Štajner, 2021).

The TSAR 2024 workshop builds upon the recent success of several regional workshops that covered a subset of our topics of interest, including READI Workshops at LREC 2022 and LREC 2024, SEPLN 2021 Workshop on Current Trends in Text Simplification (CTTS)), the SimpleText workshop at CLEF 2021, as well as the birds-of-a-feather events on Text Simplification at NAACL 2021 (over 50 participants), ACL 2022, EMNLP 2023 and EMNLP 2024.

Topics of Interest

The TSAR 2025 workshop will invite contributions on the following topics, aligned with previous editions of the workshop:

  • Lexical simplification;
  • Syntactic simplification;
  • Discourse simplification;
  • Document simplification;
  • Modular and end-to-end TS;
  • Sequence-to-sequence and zero-shot TS;
  • Controllable TS;
  • Text complexity assessment;
  • Complex word identification and lexical complexity prediction;
  • Corpora, lexical resources, and benchmarks for TS;
  • Domain specific TS (e.g. health, legal);
  • Assistive technologies for readability and comprehension beyond text.
  • Other related readability and accessibility topics (e.g. empirical and eye-tracking studies).
  • New evaluation measures and metrics for the assessment of text simplification, accessibility and/or readability;
  • Reference based metrics;
  • Referenceless metrics;
  • Metrics at varying levels of granularity (Doc/Paragraph/Sentence/Word/Sub-word);
  • The role of LLMs in text accessibility
  • Agentic and Multi-agentic systems for accessibility, readability and/or simplification
  • The role of LLMs in the evaluation of systems for text simplification, accessibility and/or readability.

Submissions

We welcome three types of papers: long papers, short papers and demos.

The papers should present novel research. The review will be double blind and thus all submissions should be anonymized.

Long and Short Papers: We adhere to the same guidelines as EMNLP 2025.

Demo Papers: Demos should be six pages in length, including references, and should describe implemented systems related to the topics of interest of the workshop. It also should include a link to a short screencast of the working software. In addition, authors of demo papers must be willing to present a demo of their system during TSAR 2025. Further guidance on the preparation of demo papers is available through the main conference website.

Program Committee

  • Anna Dmitrieva (University of Helsinki);
  • Arne Jonsson (Linköping University);
  • Christina Niklaus (University of St. Gallen);
  • Daniel Wiechmann (University of Amsterdam);
  • Daniele Schicchi (Università di Palermo);
  • David Kauchak (Pomona College);
  • Dennis Aumiller (Cohere);
  • Emad Alghamdi (King Abdulaziz University);
  • Felice Dell’Orletta (Istituto di Linguistica Computazionale “Antonio Zampolli”);
  • Freya Hewett (Humboldt Institute for Internet and Society);
  • Giulia Venturi (Institute of Computational Linguistics “Antonio Zampolli” - ILC-CNR);
  • Itziar Gonzalez-Dios (HiTZ Basque Center for Language Technologies - Ixa, University of the Basque Country UPV/EHU);
  • Jaap Kamps (University of Amsterdam);
  • Jan Trienes (University of Duisburg-Essen);
  • Jasper Degraeuwe (Ghent University);
  • Jipeng Qiang (Yangzhou University);
  • Joseph Imperial (University of Bath);
  • Laura Vásquez-Rodriguez (Idiap Research Institute);
  • Liam Cripwell (LORIA);
  • Maja Popović (ADAPT, Dublin City University);
  • Margot Madina (Darmstadt University of Applied Sciences);
  • Michael Gille (Hamburg University of Applied Science);
  • Michael Ryan (Georgia Institute of Technology);
  • Mounica Maddela (Georgia Institute of Technology);
  • Natalia Grabar (Université de Lille);
  • Oliver Alonzo (Rochester Institute of Technology);
  • Philippe Laban (Salesforce Research);
  • Piotr Przybyła (Universitat Pompeu Fabra);
  • Regina Stodden (Heinrich Heine University Düsseldorf);
  • Rémi Cardon (CENTAL, ILC, Université Catholique de Louvain);
  • Sarah Ebling (University of Zurich);
  • Silvana Deilen (University of Hildesheim);
  • Sowmya Vajjala (National Research Council Canada);
  • Susana Bautista (Universidad Francisco de Vitoria);
  • Sweta Agrawal (University of Maryland);
  • Tadashi Nomoto (National Institute of Japanese Literature);
  • Tomas Goldsack (University of Sheffield);
  • Victoria Yaneva (National Board of Medical Examiners);
  • Yannick Parmentier (University of Lorraine)