Code-switching, where speakers switch between languages within a sentence or conversation, presents unique challenges for NLP models. For example, in "I need to comprar un regalo," the switch from English to Spanish requires the model to recognize and process multiple languages seamlessly.
NLP handles code-switching by using multilingual pre-trained models like mBERT and XLM-R, which learn shared representations for multiple languages. These models leverage cross-lingual embeddings to align vocabulary and syntax across languages, enabling them to handle mixed-language inputs effectively. Fine-tuning on code-switched datasets further improves performance.
Challenges include the lack of large, annotated code-switched datasets and the diversity of language pairs and structures. Subword tokenization helps mitigate vocabulary mismatches, as it can represent words from different languages using shared subword units. While progress has been made, handling code-switching remains a complex task due to its dynamic and context-dependent nature.