Abstract: Systems, methods, and apparatuses including computer program products are provided for training machine learning systems. In some implementations, a method is provided. The method includes receiving a collection of phrases, normalizing a plurality of phrases of the collection of phrases, the normalizing being based at least in part on lexicographic normalizing rules, and generating a normalized phrase table including a plurality of key-value pairs, each key value pair includes a key corresponding to a normalized phrase and a value corresponding to one or more un-normalized phrases associated with the normalized key, each un-normalized phrase having one or more parameters.

Abstract: Systems, methods, and apparatuses including computer program products are provided for training machine learning systems. In some implementations, a method is provided. The method includes receiving a collection of phrases, normalizing a plurality of phrases of the collection of phrases, the normalizing being based at least in part on lexicographic normalizing rules, and generating a normalized phrase table including a plurality of key-value pairs, each key value pair includes a key corresponding to a normalized phrase and a value corresponding to one or more un-normalized phrases associated with the normalized key, each un-normalized phrase having one or more parameters.

Abstract: Systems, methods, and apparatuses including computer program products for machine learning are provided. A method is provided that includes distributing a parameterized model to each worker of a hierarchy of workers, the parameterized model including a plurality of feature functions and corresponding model parameters, processing a portion of training data at each worker of the plurality of workers according to the parameterized model to calculate updates to model parameters, for each worker at a lowest level of the hierarchy of workers, sending the calculated updates to a next higher level worker, for each other worker in the hierarchy of workers, combining updates of the respective worker with updates received from one or more lower level workers, collecting all updates from the workers at a master to generate real updates to the model parameters, and generating an updated model using the real updates to the model parameters.