Regional Dialect

Regional dialects pose significant challenges for natural language processing (NLP) due to their diverse linguistic features and lack of standardized resources. Current research focuses on developing robust NLP models for dialect identification and processing using techniques like energy-based models, transformer-based architectures (e.g., BERT, mT5), and data augmentation methods tailored to low-resource settings. This work is crucial for advancing equitable language technologies, improving cross-dialectal communication, and preserving linguistic diversity, particularly for endangered languages. The creation of large-scale, multi-dialectal datasets is a key priority to support these advancements.