Non Standardized Dialect

Non-standardized dialects pose significant challenges for natural language processing (NLP) due to their variations in orthography, pronunciation, and grammar compared to standard forms. Current research focuses on developing robust methods for dialect identification and evaluation, employing transformer-based models and self-supervised speech models to analyze both textual and audio data. These efforts are crucial for improving the accuracy of downstream NLP tasks like machine translation and speech recognition, and for furthering our understanding of linguistic variation and its sociolinguistic correlates, such as the impact of socioeconomic mixing on language use. The development of comprehensive benchmark datasets and improved evaluation metrics is also a key area of ongoing investigation.