Low-Resource, High-Impact: Building Corpora for Inclusive Language Technologies
It tackles data scarcity and cultural variance for NLP practitioners and researchers working with multilingual and low-resource languages to create more equitable language technologies, but it is incremental as it focuses on practical methods rather than new paradigms.
This tutorial addresses the problem of building NLP pipelines for low-resource and underrepresented languages by providing a practical toolkit for data collection, parallel sentence mining, machine translation, and downstream applications, showcasing use cases across over 10 languages.
This tutorial (https://tum-nlp.github.io/low-resource-tutorial) is designed for NLP practitioners, researchers, and developers working with multilingual and low-resource languages who seek to create more equitable and socially impactful language technologies. Participants will walk away with a practical toolkit for building end-to-end NLP pipelines for underrepresented languages -- from data collection and web crawling to parallel sentence mining, machine translation, and downstream applications such as text classification and multimodal reasoning. The tutorial presents strategies for tackling the challenges of data scarcity and cultural variance, offering hands-on methods and modeling frameworks. We will focus on fair, reproducible, and community-informed development approaches, grounded in real-world scenarios. We will showcase a diverse set of use cases covering over 10 languages from different language families and geopolitical contexts, including both digitally resource-rich and severely underrepresented languages.