Abstract: A machine learning system for a digital assistant is described, together with a method of training such a system. The machine learning system is based on an encoder-decoder sequence-to-sequence neural network architecture trained to map input sequence data to output sequence data, where the input sequence data relates to an initial query and the output sequence data represents canonical data representation for the query. The method of training involves generating a training dataset for the machine learning system. The method involves clustering vector representations of the query data samples to generate canonical-query original-query pairs in training the machine learning system.