Abstract: Systems and methods are disclosed for training a Natural Language Processing (NLP) model through iterative processing. A method involves performing multiple iterations to train the NLP model. Each iteration includes defining data points with assigned labels and preparing natural language queries for these points. A neural network processes real-world documents to generate extracted values from the real-world document to be used for querying. The extracted values are then validated by comparing them to the document data and identifying validated extracted values. The NLP model quality is evaluated using these validated values, and a quality score is determined. Upon identifying a suitable quality, the NLP model is defined as a fine-tuned model and configured to process new real-world documents for data point extraction.

Abstract: Systems and methods are disclosed for generating a Natural Language Processing (NLP) model through iterative training. A method involves processing a plurality of real-world documents, each containing text data, layout data, and image data, using at least one hardware processor. An initial prediction for data points within the documents is generated using a neural network. The initial prediction is then validated by comparing extracted values with the information present in the documents and correcting any discrepancies. The quality of the NLP model is evaluated based on the validated predictions, and upon satisfying a quality constraint, the NLP model is configured to process new documents to extract data points without further validation. This method streamlines the extraction of information from diverse document formats, enhancing the efficiency and accuracy of data retrieval in automated systems.

Abstract: Disclosed herein is a system and method for Natural Language Processing (NLP) of real world documents. the system and method combines various models not previously combined and overcomes the challenges of this combination. Models include an encoder-decoder model, a spatial model, and a multi-modal model. An iterative training process receives documents and generates outputs, wherein the iterative training process comprises enabling information retrieval from documents without training data.

Abstract: Disclosed herein is a system and method for Natural Language Processing (NLP) of real world documents. the system and method combines various models not previously combined and overcomes the challenges of this combination. Models include an encoder-decoder model, a spatial model, and a multi-modal model. An iterative training process receives documents and generates outputs, wherein the iterative training process comprises enabling information retrieval from documents without training data.

Abstract: Disclosed herein is a system and method for Natural Language Processing (NLP) of real world documents. the system and method combines various models not previously combined and overcomes the challenges of this combination. Models include an encoder-decoder model, a spatial model, and a multi-modal model. An iterative training process receives documents and generates outputs, wherein the iterative training process comprises enabling information retrieval from documents without training data.

Abstract: Disclosed herein is a system and method for Natural Language Processing (NLP) of real world documents. the system and method combines various models not previously combined and overcomes the challenges of this combination. Models include an encoder-decoder model, a spatial model, and a multi-modal model. An iterative training process receives documents and generates outputs, wherein the iterative training process comprises enabling information retrieval from documents without training data.