Facilitating generation of fillable document templates

One or more non-transitory computer-readable media having a plurality of executable instructions embodied thereon, which, when executed by one or more processors, cause the one or more processors to perform a method for converting documents to fillable documents, the method comprising:

• obtaining a set of training documents;
• identifying a group of similar training documents from the set of training documents;
• identifying text differences within the group of similar training documents;
• based on the identified text differences within the group of similar training documents, assigning a dynamic token state indicator to words in the training documents associated with the text differences;
• and
• training a machine learning model using at least a subset of the group of similar training documents and corresponding dynamic token state indicators to generate a trained machine learning model that predicts token states for text in new documents.

A system comprising:

• one or more processors;
• and
• one or more memory coupled with the one or more processors, the one or more processors to perform operations comprising:
• identifying a first document as similar to a second document;
• identifying text differences between the first document and the second document identified as similar to the first document;
• for the first document, assigning token state indicators for tokens in the first document based on the identified text differences between the first document and the second document, the token state indicators indicating whether a corresponding token is a static token or a dynamic token;
• and
• training, using the tokens in the first document and the corresponding token state indicators, a machine learning model to predict token states for text in new documents.