Structured adversarial, training for natural language machine learning tasks

A method includes obtaining first training data having multiple first linguistic samples. The method also includes generating second training data using the first training data and multiple symmetries. The symmetries identify how to modify the first linguistic samples while maintaining structural in...

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Bibliographic Details
Main Authors Goldsmith, Benjamin, Harkema, Hendrik, Stabler, Edward P
Format Patent
LanguageEnglish
Published 03.01.2023
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
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Summary:A method includes obtaining first training data having multiple first linguistic samples. The method also includes generating second training data using the first training data and multiple symmetries. The symmetries identify how to modify the first linguistic samples while maintaining structural invariants within the first linguistic samples, and the second training data has multiple second linguistic samples. The method further includes training a machine learning model using at least the second training data. At least some of the second linguistic samples in the second training data are selected during the training based on a likelihood of being misclassified by the machine learning model.
Bibliography:Application Number: US202016799495