Oracle International Corporation

Shubham Pawankumar Shah

Thanh Tien Vu

Budhaditya Saha

Omid Mohamad Nezami

Tuyen Quang Pham

Aashna Devang Kanuga

Shivashankar Subramanian

Techniques are disclosed herein for adaptive training data augmentation to facilitate training named entity recognition (NER) models. Adaptive augmentation techniques are disclosed herein that take into consideration the distribution of different entity types within training data. The adaptive augmentation techniques generate adaptive numbers of augmented examples (e.g., utterances) based on the distribution of entities to make sure enough numbers of examples for minority class entities are generated during augmentation of the training data.

Adaptive training data augmentation to facilitate training named entity recognition models

Techniques are provided for augmenting training data using gazetteers and perturbations to facilitate training named entity recognition models. The training data can be augmented by generating additional utterances from original utterances in the training data and combining the generated additional utterances with the original utterances to form the augmented training data. The additional utterances can be generated by replacing the named entities in the original utterances with different named entities and/or perturbed versions of the named entities in the original utterances selected from a gazetteer. Gazetteers of named entities can be generated from the training data and expanded by searching a knowledge base and/or perturbing the named entities therein. The named entity recognition model can be trained using the augmented training data.

Training data augmentation using gazetteers and perturbations to facilitate training named entity recognition models

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Foster City, CA, United States of America

Shubham Pawankumar Shah