In order to alleviate the huge demand for annotated datasets for different tasks, many recent
natural language processing datasets have adopted automated pipelines for fast-tracking
usable data. However, model training with such datasets poses a challenge because
popular optimization objectives are not robust to label noise induced in the annotation
generation process. Several noise-robust losses have been proposed and evaluated on
tasks in computer vision, but they generally use a single dataset-wise hyperparamter to …

There is included a method and apparatus comprising computer code for a joint training
method using neural networks with noise-robust losses comprising encoding input tokens
from a noisy dataset into input vectors using an input encoder; predicting a label based on
the input vectors using a classifier model; calculating a beta value based on the input
vectors and the label using a label quality predictor model, wherein the beta value is
instance-specific for each training instance; and j oint training more than one model using a …