Low-Resource, High-Impact: Building Corpora for Inclusive Language Technologies

TL;DR

This paper presents a practical tutorial for NLP practitioners to develop inclusive language technologies for low-resource languages, emphasizing data collection, modeling, and community engagement to address data scarcity and cultural diversity.

Contribution

It introduces a comprehensive toolkit and strategies for building NLP pipelines tailored to underrepresented languages, focusing on fairness, reproducibility, and real-world applicability.

Findings

Successful application across 10+ diverse languages

Effective methods for data collection and parallel sentence mining

Frameworks for fair and community-informed NLP development

Abstract

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.