Reverse reinforcement learning to train training data for natural language processing neural network

A computer hardware system for modifying a training dataset, comprising:

• a hardware processor configured to perform the following executable operations:
• benchmarking the training dataset using a State Of The Art (SOTA) neural network to determine a benchmark for the training dataset;
• dividing the training dataset into a plurality of slices;
• selecting, using a selection strategy generator operating on one of the plurality of slices, a sequence of a plurality of atomic operations;
• applying the sequence of the plurality of atomic operations to modify the one of the plurality of slices to generate a revised one of the plurality of slices;
• performing reverse reinforcement learning on the revised one of the plurality of slices using the benchmark and the SOTA neural network;
• and
• modifying the training dataset by replacing the one of the plurality of slices with the revised one of the plurality of slices to generate a modified training dataset.

A computer program product, comprising:

• a computer readable storage medium having stored therein program code for training a training dataset,
• the program code, which when executed by a computer hardware system, cause the computer hardware system to perform:
• benchmarking the training dataset using a State Of The Art (SOTA) neural network to determine a benchmark for the training dataset;
• dividing the training dataset into a plurality of slices;
• selecting, using a selection strategy generator operating on one of the plurality of slices, a sequence of a plurality of atomic operations;
• applying the sequence of the plurality of atomic operations to modify the one of the plurality of slices to generate a revised one of the plurality of slices;
• performing reverse reinforcement learning on the revised one of the plurality of slices using the benchmark and the SOTA neural network;
• and
• modifying the training dataset by replacing the one of the plurality of slices with the revised one of the plurality of slices to generate a modified training dataset.