Low-Resource Language Processing

Low-resource language processing studies how to build reliable text, speech, and multimodal tools when data, annotation, computing resources, or standard writing conventions are limited. Common approaches include transferring multilingual pretrained models, adapting them with continued pretraining or parameter-efficient fine-tuning, creating synthetic data, and developing language-specific tokenizers, corpora, and tools. Benchmarks for tasks such as translation, speech recognition, information retrieval, reasoning, and cultural understanding increasingly test performance across languages, dialects, scripts, and domains. A central tension is that transfer and synthetic data can reduce development costs, but gains are uneven and depend on data quality, linguistic fit, and evaluation grounded in the language community.