Ensemble NLU Scoring With Optimized Weights for Accurate Inference
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Solution Overview
Problem
Modern NLU systems face challenges in efficiently leveraging structured information from diverse data sources while maintaining scalability, handling language flexibility, and protecting sensitive data without impacting performance.
Innovation Solution
A hybrid NLU framework that includes a lookup source system with compact source data representations and encryption/obfuscation, combined with an ensemble scoring system to optimize computational resources and enhance inference accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep learning algorithms are used for NLP, then language understanding capability is improved, but computational resource consumption increases
Solution Approach 1:
The system segments the NLU pipeline into distinct stages: a rule-based component for structured information extraction (intents, entities, parameters) and a deep learning component for natural language understanding. This segmentation allows each component to operate independently with optimized resource allocation, reducing overall computational burden while maintaining high understanding capability.
Solution Approach 2:
The system dynamically adjusts the threshold for invoking deep learning algorithms based on the complexity and ambiguity of input utterances. Simple queries with clear structured information trigger only rule-based processing (low computational cost), while ambiguous or complex queries invoke deep learning models (higher computational cost), thereby optimizing resource consumption based on actual needs.
2Productivity
If source data is stored in unprotected form, then system performance is maintained, but data security and privacy are compromised
Solution Approach 1:
The system extracts and separates sensitive personally identifying information (PII) from the source data storage and processing pipeline. PII is removed before data compilation, and only non-sensitive structured information is stored and processed. This extraction eliminates the security risk of storing sensitive data while maintaining system performance through efficient processing of sanitized data.
Solution Approach 2:
The system introduces an intermediary data sanitization layer between data ingestion and processing. This intermediary component filters, cleans, and sanitizes source data to remove PII and sensitive information before the data is compiled into lookup sources. The intermediary ensures that only safe, non-sensitive data enters the processing pipeline, maintaining performance while eliminating security vulnerabilities.
3Adaptability or versatility
If fuzzy matching is implemented for language flexibility, then language adaptability is improved, but matching precision may deteriorate
Solution Approach 1:
The system dynamically adjusts the matching strategy based on the confidence level of fuzzy matches. When fuzzy matching yields low-confidence results, the system switches to exact matching or requests clarification from the user. This dynamic adaptation allows the system to maintain high precision by avoiding low-confidence fuzzy matches while still providing language flexibility when confident matches are found.
Solution Approach 2:
The system implements feedback mechanisms where the results of fuzzy matching are validated against the original utterance context. If the fuzzy match result seems incorrect or ambiguous, the system provides feedback to adjust the matching process, either by refining the fuzzy matching parameters or by seeking user clarification. This feedback loop ensures high precision while maintaining language adaptability.
Data Source
AI summary
A natural language understanding (NLU) framework includes an ensemble scoring system designed to receive indicators determined by various systems of the NLU framework when inferencing a user utterance. The ensemble scoring system uses the received indicators, along with a set of ensemble scoring weights, to determine a respective ensemble score for each artifact of the utterance identified during inference. For example, segmentations provided by a lookup source system may be used to boost scores of intent and/or entities identified during a meaning search operation of a NLU system. The NLU framework may also include an ensemble scoring weight optimization subsystem that automatically determines optimized ensemble scoring weight values from labeled training data using an optimization plugin. Accordingly, the NLU framework enables these indicators to be suitably weighted and combined to provide a desired level of performance (e.g., computational resource consumption, precision, recall) of the NLU framework during operation.