Oracle International Corporation

Yuanxu Wu

Mark Edward Johnson

Vishal Vishnoi

Srinivasa Phani Kumar Gadde

Elias Luqman Jalaluddin

Aashna Devang Kanuga

Thanh Long Duong

Cong Duy Vu Hoang

Poorya Zaremoodi

Tuyen Quang Pham

Zikai Li

Techniques are disclosed for systems including techniques for multi-feature balancing for natural langue processors. In an embodiment, a method includes receiving a natural language query to be processed by a machine learning model, the machine learning model utilizing a dataset of natural language phrases for processing natural language queries, determining, based on the machine learning model and the natural language query, a feature dropout value, generating, and based on the natural language query, one or more contextual features and one or more expressional features that may be input to the machine learning model, modifying at least one or the one or more contextual features and the one or more expressional features based on the feature dropout value to generate a set of input features for the machine learning model, and processing the set of input features to cause generating an output dataset for corresponding to the natural language query.

Multi-feature balancing for natural language processors

Techniques for data augmentation for training chatbot systems in natural language processing. In one particular aspect, a method is provided that includes generating a list of values to cover for an entity, selecting utterances from a set of data that have context for the entity, converting the utterances into templates, where each template of the templates comprises a slot that maps to the list of values for the entity, selecting a template from the templates, selecting a value from the list of values based on the mapping between the slot within the selected template and the list of values for the entity; and creating an artificial utterance based on the selected template and the selected value, where the creating the artificial utterance comprises inserting the selected value into the slot of the selected template that maps to the list of values for the entity.

Entity level data augmentation in chatbots for robust named entity recognition

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Foster City, CA, United States of America

Yuanxu Wu