Semantic Parser Generation via Canonical Statements and Validation
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Solution Overview
Problem
Conventional natural language processing systems face challenges in generating semantic parsers due to the need for large amounts of high-quality training data, inability to handle complex use cases like multi-intent requests, and lack of validation for user responses, leading to untrustworthy data.
Innovation Solution
A system and method that utilizes crowd sourcing services to generate and validate operators and utterances, employing an attribute-value structure for delexicalized operators and stochastic models to handle complex cases, and includes a canonical statement generator to create human-readable versions of operators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional systems use rapid data generation methods, then data quantity increases, but data quality and reliability deteriorate
Solution Approach 1:
The system implements validation mechanisms where generated operators and utterances are verified against canonical statements and existing knowledge bases. This feedback loop ensures that only high-quality, accurate data is incorporated into the training set, maintaining reliability while scaling quantity.
Solution Approach 2:
Canonical statements serve as intermediaries between raw user responses and the final training data. These standardized representations filter and validate information, ensuring data quality while enabling rapid generation of diverse training examples.
2Speed
If simple data structures are used, then processing speed increases, but ability to handle complex use cases deteriorates
Solution Approach 1:
The system segments complex multi-intent requests into multiple atomic operators, each representing a distinct action or intent. This segmentation allows the simple, fast operator structure to handle complex queries by composing multiple straightforward operations rather than requiring complex single structures.
Solution Approach 2:
The operator framework is designed with universal, reusable components that can function in multiple contexts. Atomic operators can be combined in various sequences to handle different intent combinations, enabling the system to process complex use cases using simple, versatile building blocks.
3Device complexity
If monolingual systems are used, then system complexity decreases, but multi-lingual processing capability deteriorates
Solution Approach 1:
The canonical statement framework provides a language-agnostic intermediate representation that enables multi-lingual processing. Utterances in different languages are translated to the same canonical form, allowing the system to handle multiple languages without duplicating processing logic, thus maintaining simplicity while achieving versatility.
4Productivity
If automated data generation is used, then productivity increases, but validation and trustworthiness deteriorate
Solution Approach 1:
The system incorporates automated validation feedback mechanisms that check generated data against canonical statements, knowledge bases, and consistency rules. This feedback ensures that high-speed automated generation does not compromise trustworthiness, as errors are detected and corrected systematically.
Solution Approach 2:
The validation system performs self-checks on generated data without requiring external manual verification. Automated consistency checks and canonical statement matching enable the system to validate its own output, maintaining reliability while preserving high productivity.
Data Source
AI summary
The disclosure relates to systems and methods for generating semantic parsers based on automatically generated operators and user-designated utterances relating to the operators for use in natural language processing. The system may automatically generate multiple operators that each express a respective computer-executable instruction that resolves a request. These operators may be expressed in a manner that is machine-readable and not necessarily for consumption by a human user. The system may generate a canonical statement that expresses the request in a first manner that a human user would be able to understand. The system may generate a task, such as crowd-sourced task, for a human user to provide an utterance that conveys the canonical statement in a second manner different than the first manner. By doing so, the system may rapidly build operators and learn how humans would utter requests resolved by instructions encoded in the operators for building semantic parsers.