Training end-to-end spoken language understanding systems with unordered entities
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United States of America Patent
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Jul 23, 2024
Grant Date -
Mar 16, 2023
app pub date -
Aug 27, 2021
filing date -
Aug 27, 2021
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Abstract
Training data can be received, which can include pairs of speech and meaning representation associated with the speech as ground truth data. The meaning representation includes at least semantic entities associated with the speech, where the spoken order of the semantic entities is unknown. The semantic entities of the meaning representation in the training data can be reordered into spoken order of the associated speech using an alignment technique. A spoken language understanding machine learning model can be trained using the pairs of speech and meaning representation having the reordered semantic entities. The meaning representation, e.g., semantic entities, in the received training data can be perturbed to create random order sequence variations of the semantic entities associated with speech. Perturbed meaning representation with associated speech can augment the training data.
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