| CPC G06F 40/289 (2020.01) [G06F 40/166 (2020.01); G06F 40/279 (2020.01); G06F 40/295 (2020.01); G06N 3/08 (2013.01); G06F 40/205 (2020.01); G06F 40/263 (2020.01); H04L 51/02 (2013.01)] | 10 Claims |
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1. A computer-implemented method, comprising:
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 natural language query, a feature dropout value for the machine learning model;
generating, based on the natural language query, a set of contextual features comprising a plurality of contextual features and a set of expressional features comprising one or more expressional features;
determining a feature correspondence between the plurality of contextual features and the one or more expressional features, to form a subset of contextual features comprising one or more contextual features corresponding to the one or more expressional features with which the feature correspondence is determined, among the plurality of contextual features;
removing, from the set of contextual features, at least a portion of the one or more contextual features of the subset of contextual features at a rate corresponding to the feature dropout value, to generate a set of modified contextual features;
generating a set of input features to include the set of modified contextual features and the one or more expressional features;
inputting, to the machine learning model, the set of input features; and
processing, by the machine learning model, the set of input features, to generate an output with respect to the natural language query,
wherein the feature dropout value comprises a first contextual feature dropout value corresponding to a first percentage of the plurality of contextual features of the set of contextual features.
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