NLP Agent Training via Reinforced Learning and Token Similarity
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Solution Overview
Problem
Training NLP agents to extract document data from diverse electronic documents and accurately match it with Systems of Record (SOR) fields is challenging due to the need for human intervention in supervised learning and the inability to utilize information gained during manual validation.
Innovation Solution
A modified reinforced learning approach is employed, using a deep Q-learning model that tokenizes documents, compares tokens with SOR fields, generates similarity scores, and adjusts the model using a loss function based on rewards, allowing for automated training without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised learning is used to train NLP agent, then document data extraction accuracy is improved, but human intervention and manual annotation are required which reduces productivity
Solution Approach 1:
The system uses reinforcement learning where the NLP agent learns autonomously through interaction with the environment and receives feedback via reward signals. The agent performs self-training by selecting actions, observing outcomes, and updating its policy without requiring manual annotation of training data, thus eliminating the need for human intervention while maintaining learning effectiveness
Solution Approach 2:
The patent transitions from supervised learning parameters (manual labels, annotated datasets) to reinforcement learning parameters (reward functions, state-action pairs). By changing the learning paradigm from supervision-based to reinforcement-based, the system achieves automated training while preserving the ability to learn accurate document data extraction patterns
2Measurement precision
If manual validation is performed to identify relationships between document data, then information accuracy is improved, but the information cannot be recorded and reused which worsens productivity
Solution Approach 1:
The system implements a feedback mechanism where the NLP agent receives reward signals based on the accuracy of its extracted document data. Correct extractions generate positive rewards that are recorded and used to update the agent's policy, enabling the system to learn from past successes and avoid repeating mistakes, thus making the validation process reusable and iterative
Solution Approach 2:
The reinforcement learning process enables continuous learning where the agent constantly refines its extraction capabilities through repeated interactions. The learned policies are stored and reused across multiple training iterations, allowing the system to continuously improve without repeating manual validation work, thus eliminating time loss while maintaining information accuracy
3Adaptability or versatility
If NLP agent is trained to handle large number of different document formats, then adaptability is improved, but the complexity of training increases due to varying document structures
Solution Approach 1:
The patent employs a universal NLP agent architecture that can process multiple document formats through a single unified model. The reinforcement learning framework enables the agent to learn format-agnostic patterns and relationships, allowing it to generalize across different document types (invoices, purchase orders, etc.) without requiring separate training models for each format, thus reducing training complexity while maintaining adaptability
Solution Approach 2:
The system uses dynamic learning where the NLP agent adapts its extraction strategy based on the specific document format encountered. The reinforcement learning policy is flexible and can adjust to varying document structures in real-time, allowing the model to handle diverse formats through a single adaptable framework rather than multiple rigid models
Data Source
AI summary
A computer-implemented process for training a natural language processing (NLP) agent having a reinforced learning model includes the following operations. A type of document from a document corpus is identified using metadata particularly associated with the document. The NLP agent tokenizes the document to generate a plurality of tokens. Using a schema identified from the type of the document, one of the plurality of tokens is compared to a system of record (SOR) field from the schema. A similarity score between the one of the plurality of tokens with a correct value and a reward based upon the similarity score are generated. A determination is made that an optimum minimum average similarity rate has not been obtained. Based upon the determination, the reinforced learning model is trained using a loss function that includes the reward.