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23 June 2025, Geneva
Scope
Scope
This full-day workshop will delve into cutting-edge technologies that advance the production of Easy and Plain Language, with a particular emphasis on Automatic Text Simplification (ATS) and the role of Large Language Models (LLMs) in generating, validating, and refining accessible communication across institutional contexts. Participants will gain a comprehensive understanding of how AI is revolutionising accessible communication, particularly through its applications in Easy and Plain Language. The workshop will offer insights into AI's potential to automate and optimise language simplification processes, while also fostering a collaborative environment for professionals to exchange best practices and experiences. By engaging with like-minded peers, attendees will be equipped to develop innovative, collaborative solutions that enhance their future work and drive progress in the field of accessible communication.
Topics
- Automating Easy Language Production: Insights into how AI can support the creation of Easy and Plain Language specialised content, reducing time and resource requirements.
- Automating Easy Language Production: Insights into how AI can support the creation of Easy and Plain Language specialised content, reducing time and resource requirements.
- Enhanced Quality Assurance: Techniques for AI-driven validation to ensure that simplified texts meet Easy Language standards, improving consistency and quality.
- Enhanced Quality Assurance: Techniques for AI-driven validation to ensure that simplified texts meet Easy Language standards, improving consistency and quality.
- Capabilities of AI Tools: Showcasing AI-powered authoring tools and resources, such as interfaces that flag complex words and constructions, analysis tools for checking text against Easy Language criteria, and simplification tools like the FALC Assistant.
- Capabilities of AI Tools: Showcasing AI-powered authoring tools and resources, such as interfaces that flag complex words and constructions, analysis tools for checking text against Easy Language criteria, and simplification tools like the FALC Assistant.
- Large Language Models (LLMs): Analysis of the strengths and limitations of LLMs for simplifying complex text into Easy Language and ensuring clarity across languages and contexts.
- Large Language Models (LLMs): Analysis of the strengths and limitations of LLMs for simplifying complex text into Easy Language and ensuring clarity across languages and contexts.
- Ethical Considerations: Exploring ethical implications of using AI for language simplification, emphasising the need for human oversight and accountability in AI-driven processes.
- Ethical Considerations: Exploring ethical implications of using AI for language simplification, emphasising the need for human oversight and accountability in AI-driven processes.
- Case Studies in Institutional Contexts: Presenting real-world examples of AI applications in various sectors, including healthcare, government, and education, where Easy and Plain Language has improved accessibility and understanding.
- Case Studies in Institutional Contexts: Presenting real-world examples of AI applications in various sectors, including healthcare, government, and education, where Easy and Plain Language has improved accessibility and understanding.
- Challenges and Limitations: Examining the technical and ethical challenges associated with using AI for language simplification, including potential limitations in accuracy, cultural context, and nuance.
- Challenges and Limitations: Examining the technical and ethical challenges associated with using AI for language simplification, including potential limitations in accuracy, cultural context, and nuance.
- Future of AI and Plain Language: Projections on how AI developments might shape the future of language accessibility, with a focus on institutional needs and user inclusivity.
- Future of AI and Plain Language: Projections on how AI developments might shape the future of language accessibility, with a focus on institutional needs and user inclusivity.
Important dates
First call for papers: 20 December 2024
Submission deadline:
17 March 2025 (23:59 AOE Anywhere on Earth)
Notification of acceptance: 7 April 2025
Camera-ready version deadline: 21 April 2025
Proceedings: 9 June 2025
Workshop date: 23 June 2025
Organisation Committee
Organisation Committee
María Isabel Rivas Ginel, Dublin City University
Patrick Cadwell, Dublin City University
Paolo Canavese, Université de Genève
Silvia Hansen-Schirra, Johannes Gutenberg University
Martin Kappus, ZHAW School of Applied Linguistics
Anna Matamala, Universitat Autònoma de Barcelona
Will Noonan, Université de Bourgogne
Questions, please contact
Questions, please contact
ai-easy-and-plain-language-workshop@googlegroups.com
ai-easy-and-plain-language-workshop@googlegroups.com
Programme Committee
Programme Committee
Łucja Biel, University of Warsaw
Katy Brickley, Kings College
Luisa Carrer, Zurich University of Applied Sciences
Iria da Cunha, Universidad Nacional de Educación a Distancia
Silvana Deilen, Hildesheim University
Carlo Eugeni, University of Leeds
Annarita Felici, University of Geneva
Itziar Gonzalez-Dios, University of the Basque Country (UPV/EHU)
Silke Gutermuth, Johannes Gutenberg-Universität Mainz
Sergio Hernández Garrido, Hildesheim University
Freya Hewett, University of Potsdam, Humboldt Institute for Internet and Society
Ekaterina Lapshinova-Koltunski, Hildesheim University
Sarah McDonagh, Universitat Autònoma de Barcelona
Joss Moorkens, Dublin City University
Jean Nitzke, University of Agder
Katharina Oster, Johannes Gutenberg-Universität Mainz
Elisa Perego, University of Pavia
Maja Popovic, Dublin City University
Caroline Rossi, Université Grenoble Alpes
Regina Stodden, Heinrich Heine University
Jesús Torres del Rey, Universidad de Salamanca
Jan Trienes, University of Marburg
Ulla Vanhatalo, University of Helsinki
Giulia Venturi, Istituto di Linguistica Computazionale "Antonio Zampolli"
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