Code Switching Data

Code-switching data, encompassing speech and text where speakers alternate between two or more languages within a single utterance, is a growing area of research focusing on improving multilingual natural language processing (NLP) models. Current efforts concentrate on developing robust methods for generating and analyzing code-switched data, often employing techniques like progressive training and data augmentation to address challenges posed by limited resources and the inherent complexity of code-switching phenomena. This research is crucial for advancing multilingual NLP capabilities, particularly in speech recognition and sentiment analysis, and for creating more inclusive and accurate language technologies.