Abstract: A system and method of training a machine-learning (ML) based model by at least one processor may include receiving an initial dataset, including a plurality of annotated data samples; based on the initial dataset, training at least one ML-based first-level model to perform a first-level task; based on training the at least one ML-based first-level model, calculating at least one characteristic, representing, for each data sample, a value of contribution into a training of a ML-based second-level model to perform a second-level task; omitting a subset of data samples from the initial dataset based on the at least one characteristic, to obtain a target dataset; and training ML-based second-level model, to perform the second-level task, based on the target dataset.

Abstract: A method for training a multi-class classification model includes receiving training data corresponding to a plurality of classes. For each class in the plurality of classes, the method includes training a binary classification model configured to determine whether or not an observation of training data belongs to the class and for each observation of training data identified as belonging to the class, extracting one or more class identification features from the observation of training data based on activations of an intermediate attention layer in the binary classification model. A multi-class classification model is trained using the class identification features extracted for each of the plurality of classes.

Abstract: A computer implemented method of document classification includes receiving a text document. A first classification is generated for the document, and a text corpus is searched for one or more terms from the document. Searched terms having an incidence in the text corpus lower than a threshold incidence are flagged, and at least one classification is generated after removing at least one flagged term from the document. An output is generated if the further classification is different from the first classification.

Abstract: A method for training a multi-class classification model includes receiving training data corresponding to a plurality of classes. For each class in the plurality of classes, the method includes training a binary classification model configured to determine whether or not an observation of training data belongs to the class and for each observation of training data identified as belonging to the class, extracting one or more class identification features from the observation of training data based on activations of an intermediate attention layer in the binary classification model. A multi-class classification model is trained using the class identification features extracted for each of the plurality of classes.