Structured Translation Models for Verifying Natural Language Standards
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Solution Overview
Problem
Existing systems and protocols are often written in unstructured natural language, making it difficult for computer-based tools to verify consistency and completeness, as they lack the ability to understand natural language.
Innovation Solution
A computing system generates random trees or graphs based on a grammar with defined biases, converting natural language into structured representations using a machine learning model trained on labeled pairs, enabling translation into domain-specific symbolic language.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If standards documents are written in natural language for human consumption, then human understanding is improved, but computer-based tools lack the ability to understand and verify consistency and completeness
Solution Approach 1:
The patent introduces an intermediary structured representation format that sits between natural language and computer-based verification tools. This structured representation serves as a mediator that preserves the human-readable aspects of natural language while adding machine-understandable structure, enabling both human comprehension and automated verification of standards documents.
Solution Approach 2:
The patent segments natural language standards documents into structured representations by breaking down the text into discrete, organized components. This segmentation process divides the continuous natural language flow into structured elements that can be individually processed and verified by computer-based tools, maintaining the original meaning while enabling automated analysis.
2Ease of operation
If natural language is used in standards documents, then human consumption is facilitated, but ambiguities inherent to natural language arise
Solution Approach 1:
The structured representation acts as an intermediary that eliminates ambiguity by providing a precise, unambiguous format while maintaining the natural language content. This mediator layer ensures that the meaning intended in natural language is preserved without the ambiguities that naturally occur in human language, enabling reliable automated processing.
Solution Approach 2:
The patent changes the parameter of representation from unstructured natural language to structured format. This parameter change transforms the document from a state prone to ambiguity into a state with precise, defined structure, while preserving the original natural language meaning through the structured representation.
3Productivity
If computer-based tools are used to verify standards documents, then automated verification is achieved, but the tools lack the ability to understand natural language
Solution Approach 1:
The structured representation serves as a mediator that bridges the gap between computer-based verification tools and natural language standards documents. This intermediary format enables automated tools to process and verify standards documents by providing a machine-understandable structure while preserving the original natural language content for verification purposes.
Solution Approach 2:
The patent substitutes the mechanical capability of natural language understanding with a structured representation system. Instead of requiring computer tools to understand natural language directly, the system replaces this complex capability with a structured format that can be processed by standard automated verification tools, achieving the same goal through a different mechanism.
Data Source
AI summary
In general, the disclosure describes techniques for machine learning for translation to structured computer readable representation. An example method to generate a training set for a natural language translation model includes receiving, by a computing system, a grammar comprising rules, one or more of the rules being associated with random biases; generating, by the computing system, at least one of random trees or random graphs based on the random biases in the grammar; for each of the random trees or random graphs, by the computing system, generating a natural language sample; and generating, by the computing system, the training set with the random trees or random graphs and the corresponding natural language samples.