Despite rapid progress in Natural Language Processing (NLP), the benefits of recent advances - especially large language models (LLMs) - remain unevenly distributed. While high-resource languages like English, French, and Chinese have seen significant performance gains, low-resource languages continue to face substantial challenges across core NLP tasks such as machine translation, sentiment analysis, named entity recognition (NER), and part-of-speech tagging.
These disparities arise from a combination of factors: the scarcity of high-quality training data, limited linguistic resources, and a lack of community involvement in data collection and model development. As a result, many languages, particularly African, Indigenous, and minority languages, remain underrepresented in both academic research and deployed NLP systems.
LowResNLP is a workshop dedicated to addressing these challenges by fostering research, collaboration, and discussion around methods, resources, and evaluation practices specifically designed for low-resource languages. LowResNLP seeks to actively contribute to the field by inviting submissions that specifically address the unique challenges and opportunities involved in working with low-resource languages.
Stay tuned for updates as we approach the workshop date!
We look forward to your submissions and participation in Varna!