Computer-implemented contract risk assessment platform leveraging transformers

A non-transitory computer-readable storage medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations for automatic identification and display of segments of text in computer-readable documents, the operations comprising:

• receiving a document provided as a computer-readable file;
• receiving a set of questions;
• for each question in the set of questions, generating an inference input comprising a question, at least a portion of text of the document, and multiple tokens;
• processing, by a pre-trained language model (PLM), the inference input to generate a set of text embeddings;
• processing, by a neural network, the set of text embeddings to provide sets of tokens, each set of tokens being specific to a segment of the document and comprising a start token and an end token respectively identifying a start position and an end position of the segment;
• determining, from the sets of tokens, a segment for display by determining a score difference based on a null score and a non-null score and selecting the segment;
• and
• displaying at least a portion of the document in a user interface (UI) and an annotation indicating the segment within the at least a portion of the document.

A system, comprising:

• a computing device;
• and
• a computer-readable storage device coupled to the computing device and having instructions stored thereon which, when executed by the computing device, cause the computing device to perform operations for automatic identification and display of segments of text in computer-readable documents, the operations comprising:
• receiving a document provided as a computer-readable file;
• receiving a set of questions;
• for each question in the set of questions, generating an inference input comprising a question, at least a portion of text of the document, and multiple tokens;
• processing, by a pre-trained language model (PLM), the inference input to generate a set of text embeddings;
• processing, by a neural network, the set of text embeddings to provide sets of tokens, each set of tokens being specific to a segment of the document and comprising a start token and an end token respectively identifying a start position and an end position of the segment;
• determining, from the sets of tokens, a segment for display by determining a score difference based on a null score and a non-null score and selecting the segment;
• and
• displaying at least a portion of the document in a user interface (UI) and an annotation indicating the segment within the at least a portion of the document.