Systems and methods for machine learning-based data field validation

A method for machine learning-based data field validation of free-text inputs using a Siamese triplet long-short term model (LSTM) network architecture, the method comprising:

• receiving, at a data receiver interface, a dataset having one or more positively labelled records and one or more negatively labelled records, the dataset having a class imbalance between a number of positively labelled records and a number of negatively labelled records;
• instantiating, by a machine learning training engine, an untrained Siamese triplet LSTM network architecture including a first LSTM configured for learning a positive class embedding, a second LSTM configured for learning an anchor class embedding, and a third LSTM configured for learning negative class embedding in their corresponding latent spaces, the first LSTM, the second LSTM, and the third LSTM having identical sub-network architecture configurations;
• generating, by the machine learning training engine, a set of training tuples from the dataset, the set of training tuples expanding a number of records of the dataset by including extended training examples based on different combinations of a record of the positively labelled records or the negatively labelled records being assigned as an anchor coupled with two other records as positive and negatively records such that each training tuple contains a positive record, an anchor record, and a negative record;
• training, by the machine learning training engine, the first LSTM, the second LSTM and the third LSTM using a loss function that is configured to minimize a distance between the positive record and the anchor record and maximize a distance between the anchor record and the negative record;
• receiving, at the data receiver interface, a new data object for classification;
• generating one or more inference tuples where the new data object is represented as the anchor record, and a positive record and a negative record are obtained from two other records;
• and
• processing the one or more inference tuples to generate a classification output logit representative of a confidence score associated with a classification label of positive or negative.

A non-transitory computer readable medium storing machine interpretable instructions, which when executed by a processor, cause the processor to perform a method A method for machine learning-based data field validation of free-text inputs using a Siamese triplet long-short term model (LSTM) network architecture, the method comprising:

• receiving, at a data receiver interface, a dataset having one or more positively labelled records and one or more negatively labelled records, the dataset having a class imbalance between a number of positively labelled records and a number of negatively labelled records;
• instantiating, by a machine learning training engine, an untrained Siamese triplet LSTM network architecture including a first LSTM configured for learning a positive class embedding, a second LSTM configured for learning an anchor class embedding, and a third LSTM configured for learning negative class embedding in their corresponding latent spaces, the first LSTM, the second LSTM, and the third LSTM having identical sub-network architecture configurations;
• generating, by the machine learning training engine, a set of training tuples from the dataset, the set of training tuples expanding a number of records of the dataset by including extended training examples based on different combinations of a record of the positively labelled records or the negatively labelled records being assigned as an anchor coupled with two other records as positive and negatively records such that each training tuple contains a positive record, an anchor record, and a negative record;
• training, by the machine learning training engine, the first LSTM, the second LSTM and the third LSTM using a loss function that is configured to minimize a distance between the positive record and the anchor record and maximize a distance between the anchor record and the negative record;
• receiving, at the data receiver interface, a new data object for classification;
• generating one or more inference tuples where the new data object is represented as the anchor record, and a positive record and a negative record are obtained from two other records;
• and
• processing the one or more inference tuples to generate a classification output logit representative of a confidence score associated with a classification label of positive or negative.