Machine learning system for digital assistants

A machine learning framework for digital assistants that uses an encoder–decoder sequence-to-sequence model to transform user queries into canonical representations. Training data is generated by clustering query embeddings to create canonical–original pairs, enabling scalable learning and improved query normalization.