Abstract
This tutorial (this https URL) 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.
Abstract (translated)
本教程（此 HTTPS URL）专为从事多语言和低资源语言工作的自然语言处理从业人员、研究人员和开发人员设计，旨在创建更加公平且具有社会影响力的语言技术。参与者将获得一套实用工具包，用于构建面向边缘化语言的端到端自然语言处理管道——从数据收集和网络抓取到平行句子挖掘、机器翻译以及文本分类和多模态推理等下游应用。本教程提供了应对数据稀缺和文化差异挑战的战略，并提供动手操作的方法和建模框架。我们将重点关注公平、可重复且以社区为导向的发展方法，这些方法基于现实世界的情况。我们将展示一系列多样化的用例，涵盖来自不同语言家族和地区背景的10多种语言，包括数字化资源丰富和严重边缘化使用的语言。
URL
https://arxiv.org/abs/2512.14576
