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

Claim 0:
1. A method for generating labeled textual training data, the method comprising:
receiving, with a processor, a first unit of text data;
generating, with the processor, a second unit of text data based on the first unit of text data using a first machine learning model;
determining, with the processor, a label describing a feature of the second unit of text data using a second machine learning model;
outputting, with an output device, the second unit of text data and the label to a user;
receiving, via a user interface, one of a correction to the second unit of text data and a verification of the second unit of text data; and
retraining, with the processor, the second machine learning model using 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.