Under the hood: Multilingual embeddings

Introduced multilingual embeddings for NLP tasks, enabling text classification across languages without explicit translation. This involves training language-specific embeddings using fastText, projecting them into a common space using an orthogonal matrix derived from parallel data, and integrating them into the DeepText framework. This approach significantly improves cross-lingual accuracy and reduces latency compared to translation-based methods.