Techniques for two-stage entity-aware data augmentation

A non-transitory computer-readable medium storing computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• accessing a first set of training data for an intent prediction model, the first set of training data comprising utterances and intent labels;
• extracting named entities from the first set of training data using a trained Named Entity Recognition (NER) model;
• applying one or more data augmentation techniques to the first set of training data, to result in a second set of training data;
• applying an additional augmentation technique to augment the second set of training data to create a post-processed augmented training data,
• wherein the additional augmentation technique comprises replacing at least one or more entity values of the named entities within the second set of training data with random values of the same entity type;
• combining the first set of training data and the post-processed augmented training data to generate expanded training data;
• training the intent prediction model using the expanded training data;
• and
• deploying the trained intent prediction model.

A system, comprising:

• one or more processors;
• and
• one or more non-transitory computer readable media storing computer-executable instructions that, when executed by the one or more processors, cause the system to perform:
• accessing a first set of training data for an intent prediction model, the first set of training data comprising utterances and intent labels;
• extracting named entities from the first set of training data using a trained Named Entity Recognition (NER) model;
• applying one or more data augmentation techniques to the first set of training data, to result in a second set of training data;
• applying an additional augmentation technique to augment the second set of training data to create a post-processed augmented training data,
• wherein the additional augmentation technique comprises replacing at least one or more entity values of the named entities within the second set of training data with random values of the same entity type;
• combining the first set of training data and the post-processed augmented training data to generate expanded training data;
• training the intent prediction model using the expanded training data;
• and
• deploying the trained intent prediction model.