Batching techniques for handling unbalanced training data for a chatbot
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United States of America
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Feb 25, 2025
Grant Date -
Sep 30, 2021
app pub date -
Mar 30, 2021
filing date -
Mar 30, 2021
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Abstract
The present disclosure relates to chatbot systems, and more particularly, to batching techniques for handling unbalanced training data when training a model such that bias is removed from the trained machine learning model when performing inference. In an embodiment, a plurality of raw utterances is obtained. A bias eliminating distribution is determined and a subset of the plurality of raw utterances is batched according to the bias-reducing distribution. The resulting unbiased training data may be input into a prediction model for training the prediction model. The trained prediction model may be obtained and utilized to predict unbiased results from new inputs received by the trained prediction model.
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