Training a Model in a Data-Scarce Environment Using Added Parameter Information

A training process produces a machine-learned model that, once trained, can be applied to process different types of data items. The training process accomplishes this result by combining data items in a training set with type-specific parameter information, to produce supplemented data items. The training process then trains a model based on the supplemented data items. Training involves adjusting model weights together with the type-specific parameter information. In an inference stage of processing, the technology combines a new data item with an appropriate type of trained parameter information, and then maps the resultant supplemented data item to an output data item. The technology is particularly effective in adapting an initial model to a new subject matter domain in those situations in which a robust set of data items that pertain to the subject matter domain and which have a desired type is lacking.