Lookup Source Templates for Low-Latency Secure NLU Matching
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Solution Overview
Problem
Modern NLU systems face challenges in efficiently leveraging structured information from diverse data sources while maintaining scalability, minimizing computational resources, and protecting sensitive data, particularly in handling language flexibility and privacy concerns.
Innovation Solution
A hybrid NLU framework utilizing a lookup source system with inverse finite state transducers and configurable matchers for efficient data representation and matching, enabling exact and fuzzy matching, and protecting sensitive data through encryption and obfuscation, while supporting parallel processing and caching for low latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a NLU system leverages structured information from diverse data sources to enhance domain specificity, then intent and entity detection improves, but system complexity and computational resource requirements increase
Solution Approach 1:
The system segments the data processing pipeline into distinct phases: compile-time processing where source data is transformed into lookup source templates, and inference-time processing where templates are applied to user utterances. This segmentation allows complex data transformation to occur once during compilation, while inference remains simpler and faster, resolving the contradiction between detection accuracy and system complexity.
Solution Approach 2:
The system performs preliminary transformation of source data into optimized lookup source templates during compile-time operation. This preliminary action prepares the data in advance, so that during inference-time operation, the system only needs to apply pre-processed templates rather than processing raw source data, thereby improving detection accuracy while reducing real-time computational complexity.
2Object-affected harmful factors
If the system implements data protection techniques for sensitive data, then data security improves, but processing time and computational resources increase
Solution Approach 1:
The system applies data protection techniques during compile-time operation when source data is transformed into lookup source templates. By encrypting or obfuscating sensitive data at this preliminary stage, the system protects data security before it enters the inference pipeline, while avoiding the need for repeated protection operations during each inference request, thus minimizing processing time overhead.
Solution Approach 2:
The system creates protected copies of sensitive data in the form of lookup source templates during compilation. These templates contain encrypted or obfuscated versions of sensitive information that can be efficiently applied during inference without requiring the full original sensitive data to be processed repeatedly, thereby maintaining security while reducing processing time.
3Adaptability or versatility
If the system uses deep learning approaches for NLP, then language understanding capability improves, but computational resources and processing time increase
Solution Approach 1:
The system segments the NLP processing into two distinct phases: compile-time transformation where source data is processed into templates, and inference-time application where templates are matched against user utterances. This segmentation allows the system to use computationally intensive methods during compilation to create optimized representations, while the inference phase uses lighter-weight template matching, thereby reducing overall computational resource consumption while maintaining language understanding capability.
Solution Approach 2:
The system performs preliminary processing of source data into optimized lookup source templates during compile-time operation. This preliminary action pre-computes the necessary transformations and optimizations, so that during inference-time operation, the system can use more efficient template-based matching rather than re-processing the original source data with computationally intensive deep learning models, thereby reducing real-time computational resource requirements.
Data Source
AI summary
A natural language understanding (NLU) framework includes a lookup source framework that enables the lookup sources to be created and applied to understanding utterances. Each lookup source is associated with a respective lookup source template that defines the compile-time and inference-time behavior of the lookup source. For example, a lookup source template indicates which plugins are used by the lookup source, and may define property values that determine the operational behavior of each of these plugins during compile-time and/or inference-time operation of the lookup source. The lookup source framework includes a template manager that manages lookup source templates and determines a suitable lookup source template for each lookup source. The lookup source framework includes a lookup source template optimization subsystem that can apply a suitable optimization plugin to automatically determine attribute values to be included in an optimized lookup source template of a lookup source of the lookup source system.