Domain-specific processing and information management using extractive question answering machine learning and artificial intelligence models

A method of domain-adaptation for an extractive question answering (QA) machine learning network, the method comprising:

• obtaining a domain-adapted machine learning (ML) network, wherein the domain-adapted ML network is a trained information extraction ML network trained using a domain-specific training dataset including a plurality of training data inputs corresponding to one or more of a domain or a lexicon of domain-specific terminology;
• performing a first fine-tuning training of the domain-adapted ML network to yield a domain-adapted general QA ML network, the first fine-tuning using a first question answering (QA) dataset comprising a first plurality of question-answer training pairs, wherein the first plurality of question-answer training pairs do not correspond to the lexicon of the domain-specific terminology;
• and
• performing a second fine-tuning training of the domain-adapted general QA ML network to yield a fine-tuned domain-adapted general QA ML network, the second fine-tuning using (1) a second QA dataset comprising a second plurality of question-answer pairs generated based on a corpus of text narratives utilizing the lexicon of the domain-specific terminology, wherein the corpus of text narratives are extracted from various documents and image artifacts of the domain using at least natural language processing, and (2) a decision tree of pre-defined category-wise questions, wherein the decision tree enables the model to sequentially extract relevant portions of the corpus of text narratives and minimizing redundant questions.