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

Claim 15:
16. A system for generating labeled textual training data, the system comprising:
an output device;
a user interface;
a memory configured to store a plurality of units of text data, each respective unit of text data in the plurality of units of text data having a respective label describing a feature of the respective unit of text data;
a processor operably connected to the output device, the user interface, and the memory, the processor being configured to
read a first unit of text data from the plurality of units of text data stored on the memory;
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 the feature of the second unit of text data using a second machine learning model;
operate the output device to output the second unit of text data and the label to a user;
operate the 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;
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; and
retrain the first machine learning model using the one of the corrected second unit of text data and the verified second unit of text data,

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 a multi-reward optimization that reinforces a plurality of reward functions, the plurality of reward functions including a first 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.