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Natural Language Processing (NLP) has witnessed remarkable progress in recent years, largely driven by the emergence of deep learning architectures and, more recently, large language models (LLMs). Nevertheless, these advances have disproportionately benefited high-resource languages that possess abundant data for model training. By contrast, low-resource languages – which account for at least 85% of the world’s linguistic diversity and are often spoken by smaller or marginalised communities – have not yet reaped the full benefits of contemporary NLP technologies.
This imbalance can be attributed to several interrelated factors, including the scarcity of high-quality training data, limited computational and financial resources, and insufficient community engagement in data collection and model development. Developing NLP applications for low-resource languages poses major challenges, particularly the need for large, well-annotated datasets, standardised tools, and robust linguistic resources.
Neural language models have transformed NLP, achieving state-of-the-art performance across a wide range of tasks. However, their effectiveness remains heavily dependent on the availability of extensive pre-training resources. Consequently, language models often exhibit limited performance when applied to low-resource languages, both during training and evaluation. In response, there has been a growing research movement dedicated to the development and adaptation of language models tailored to low-resource settings.
Although several workshops have previously addressed NLP for low-resource languages, LaTeLL represents the first international conference dedicated specifically to the automatic processing of such languages. The event aims to provide a forum for researchers to present and discuss their latest work in NLP in general, and in the development and evaluation of language models for low-resource languages in particular.
The Final Call for Papers can be downloaded here.
Кeynote speaker
Nizar Habash
Professor of Computer Science, New York University Abu Dhabi, UAE
He is also the director of the Computational Approaches to Modeling Language (CAMeL) Lab. Professor Habash specializes in natural language processing and computational linguistics. Before joining NYUAD in 2014, he was a research scientist at Columbia University’s Center for Computational Learning Systems. He received his PhD in Computer Science from the University of Maryland College Park in 2003. He has two bachelors degrees, one in Computer Engineering and one in Linguistics and Languages. His research includes extensive work on machine translation, morphological analysis, and computational modeling of Arabic and its dialects. Professor Habash has been a principal investigator or co-investigator on over 30 research grants. And he has over 300 publications including a book entitled “Introduction to Arabic Natural Language Processing”. Professor Habash is one of the inaugural recipients of the King Salman Academy for Arabic Language Award (2022); he is the recipient of the Antonio Zampolli Prize (2024); and he was selected as a Fellow of the Association for Computational Linguistics (2025).
Conference Chair
Ruslan Mitkov (Lancaster University, UK and University of Alicante, Spain)
Programme Committee Chairs
Saad Ezzini (King Fahd University of Petroleum & Minerals, Saudi Arabia)
Salima Lamsiyah (University of Luxembourg, Luxembourg)
Tharindu Ranasinghe (Lancaster University, UK)
Organising Committee
Radouane Achamlal – Chair Local Arrangements
Ismail Berrada – Local Chair
Maram Alharbi (Lancaster University, UK)
Salmane Chafik (Mohammed VI Polytechnic University, Morocco)
Divya (Lancaster University, UK)
Ernesto Luis Estevanell-Valladares (University of Alicante, Spain and University of Havana, Cuba)
Milica Ikonić Nešić (University of Belgrade, Serbia)