Neural network-facilitated linguistically complex message generation systems and methods

Abstract

Provided are methods and systems for automated or semi-automated generation of complex messages. Provided systems include neural network(s) that are trained with at least an initial training set including message records having specific characteristics, such as size and form characteristics, and recognize certain user inputted content as 'instructional prompts.' The neural network(s) use the instructional prompts, training set, and other prompts to generate a distribution of semantic element options for each semantic element the system determines to include in system drafted message(s). The system selects from among such options to generate a plurality of draft messages which are presented to users for evaluation, editing, or transmission, with the instructional prompts treated as priority content. The systems and methods include mechanisms for reviewing and changing the instructional prompts based on factors that can include the content of the system-generated draft messages before further iterations to enhance the accuracy of future messages.