Generating dual sequence inferences using a neural network model

A system for training a neural network model, the system comprising:

• a memory configured to store the neural network model;
• and
• a processor configured to:
• generate, using the neural network model, a series of inferences from a first training sequence and a second training sequence;
• generate a mixed learning objective from the series of inferences, wherein to generate the mixed learning objective the processor is further configured to:
• determine, using a supervised learning objective, a first loss or a first reward for an inference in the series of inferences independently of other inferences in the series of inferences;
• determine, using a reinforcement learning objective, a second loss or a second reward over the series of inferences by:
• determine a baseline score using a scoring function, wherein the baseline score is based on baseline start and end positions of an answer span and a ground truth start and end positions of the answer span;
• determine a reinforcement learning reward function based on the scoring function and the baseline score, wherein the reinforcement learning reward function is based on the ground truth start and end positions of the answer span and a start and an end positions of an inference answer span;
• and
• determine the second loss or the second reward based on the reinforcement learning reward function;
• and
• combine the supervised learning objective and the reinforcement learning objective into a mixed objective using the first and second loss or the first and second reward;
• and
• update parameters of the neural network model based on the mixed learning objective.

A non-transitory computer readable medium having instructions stored thereon, that when executed by a processor cause the processor to perform operations for training a neural network model, the operations comprising:

• generating, using the neural network model, a series of inferences from a training sequence;
• generating a mixed learning objective from the series of inferences, wherein generating the mixed learning objective further comprises:
• determining, using a supervised learning objective, a first loss or a first reward for an inference in the series of inferences independently of other inferences in the series of inferences;
• determining, using a reinforcement learning objective, a second loss or a second reward over the series of inferences by:
• determining a baseline score using a scoring function, wherein the baseline score is based on baseline start and end positions of an answer span and a ground truth start and end positions of the answer span;
• determining a reinforcement learning reward function based on the scoring function and the baseline score, wherein the reinforcement learning reward function is based on the ground truth start and end positions of the answer span and a start and end positions of an inference answer span;
• and
• determining the second loss or the second reward based on the reinforcement learning reward function;
• and
• combining the supervised learning objective and the reinforcement learning objective into a mixed objective using the first and second loss or the first and second reward;
• and
• updating parameters of the neural network model based on the mixed learning objective.