Multi-branch machine learning models for multi-domain and multi-task processing

A processor-implemented method, comprising:

• extracting, using a first neural network block, a plurality of first features from a received input;
• identifying, using a gating neural network and the plurality of first features, a second neural network block from a plurality of second neural network blocks to use in processing the received input, each neural network in the plurality of second neural network blocks being trained to process data from a cluster of related domains in a universe of domains;
• and
• processing the received input using the identified second neural network block from the plurality of second neural network blocks.

A processing system, comprising:

• at least one memory having executable instructions stored thereon;
• and
• one or more processors configured to execute the executable instructions to cause the processing system to:
• extract, using a first neural network block, a plurality of features associated with inputs in a multi-domain input data set;
• generate a confusion matrix based on the extracted plurality of features;
• identify a plurality of clusters from the confusion matrix, each cluster in the plurality of clusters corresponding to one or more data domains in the multi-domain input data set;
• train a first gating neural network based on the multi-domain input data set and the identified plurality of clusters;
• and
• train a plurality of second neural network blocks based on a division of the multi-domain input data set into data associated with each cluster of the plurality of clusters.

A processor-implemented method, comprising:

• extracting, using a first neural network block, a plurality of features associated with inputs in a multi-domain input data set;
• generating a confusion matrix based on the extracted plurality of features;
• identifying a plurality of clusters from the confusion matrix, each cluster in the plurality of clusters corresponding to one or more data domains in the multi-domain input data set;
• training a first gating neural network based on the multi-domain input data set and the identified plurality of clusters;
• and
• training a plurality of second neural network blocks based on a division of the multi-domain input data set into data associated with each cluster of the plurality of clusters.