Patent ID: 11875120
Assignee: ROBERT BOSCH GMBH
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 16:
17. A non-transitory computer-readable medium for generating labeled textual training data, the non-transitory computer-readable medium storing program instructions that, when executed by a processor, cause the processor to:
receive a first unit of text data;
generate a second unit of text data based on the first unit of text data using a first machine learning model;
determine a label describing a feature of the second unit of text data using a second machine learning model;
operate an output device to output the second unit of text data and the label to a user;
operate a user interface to receive (i) one of a correction to the second unit of text data and a verification of the second unit of text data, and (ii) one of a correction of the label and a verification of the label; and
retrain the second machine learning model using (i) one of the corrected second unit of text data and the verified second unit of text data, and (ii) one of the corrected label and the verified label,
wherein, prior to generating the second unit of text data, the first machine learning model is trained based on a plurality of units of text data using a reinforcement learning process that includes optimizing parameters of the first machine learning model using an optimization that reinforces a reward function that, given a respective input sequence of tokens to the first machine learning model, rewards respective output sequences of tokens generated by the first machine learning model for which at least one of (i) an uncertainty and (ii) an entropy of the second machine learning model is relatively higher in determining respective labels describing the feature of the respective output sequences of tokens.