Abstract: A computer-implemented system and method for identifying citations within regulatory content is disclosed. The method involves receiving image data representing a format and layout of the regulatory content, receiving a language embedding including a plurality of tokens representing words or characters in the regulatory content, and generating a token mapping associating each of the tokens with a portion of the image data. The method also involves receiving the plurality of tokens and token mapping at an input of a citation classifier, the citation classifier having been trained to generate a classification output for each token based on the language embedding and the token mapping, the classification output identifying a plurality of citation tokens within the plurality of tokens.

Abstract: A computer-implemented method for generating regulatory content requirement descriptions is disclosed and involves receiving requirement data including a plurality of requirements including hierarchical information extracted from regulatory content. The method involves identifying parent requirements based on the existence of child requirements on a lower hierarchical level and generating requirement pairs including the parent requirement and at least one child requirement. The method also involves feeding each of the pairs through a conjunction classifier which has been trained to generate a classification output indicative of the pair being not a conjunction (NC), a single requirement conjunction (CSR), or a multiple requirement conjunction (CMR). The method involves generating a set of requirement descriptions based on the classification output generated for each parent requirement.

Abstract: A neural network system for performing a processing task on regulatory content and a method for training the system are disclosed. The method for training involves configuring a neural network language model capable of generating a language embedding output in response to receiving content. The method further involves fine-tuning the language model using regulatory content training data to generate a regulatory content language embedding output for regulatory content processed by the language model. The method also involves configuring at least one task specific output layer to generate task specific results in response to receiving the regulatory content language embedding output from the language model, and training the neural network system using task specific training data to output the task specific results, at least a portion of the task specific training data having been labeled prior to configuring the task specific neural network.

Abstract: A computer-implemented method for generating regulatory content requirement descriptions is disclosed and involves receiving requirement data including a plurality of requirements including hierarchical information extracted from regulatory content. The method involves identifying parent requirements based on the existence of child requirements on a lower hierarchical level and generating requirement pairs including the parent requirement and at least one child requirement. The method also involves feeding each of the pairs through a conjunction classifier which has been trained to generate a classification output indicative of the pair being not a conjunction (NC), a single requirement conjunction (CSR), or a multiple requirement conjunction (CMR). The method involves generating a set of requirement descriptions based on the classification output generated for each parent requirement.

Abstract: A neural network system for performing a processing task on regulatory content and a method for training the system are disclosed. The method for training involves configuring a neural network language model capable of generating a language embedding output in response to receiving content. The method further involves fine-tuning the language model using regulatory content training data to generate a regulatory content language embedding output for regulatory content processed by the language model. The method also involves configuring at least one task specific output layer to generate task specific results in response to receiving the regulatory content language embedding output from the language model, and training the neural network system using task specific training data to output the task specific results, at least a portion of the task specific training data having been labeled prior to configuring the task specific neural network.

Abstract: A computer-implemented system and method for identifying citations within regulatory content is disclosed. The method involves receiving image data representing a format and layout of the regulatory content, receiving a language embedding including a plurality of tokens representing words or characters in the regulatory content, and generating a token mapping associating each of the tokens with a portion of the image data. The method also involves receiving the plurality of tokens and token mapping at an input of a citation classifier, the citation classifier having been trained to generate a classification output for each token based on the language embedding and the token mapping, the classification output identifying a plurality of citation tokens within the plurality of tokens.