Semantic Similarity-Based Caching for NLU Skill Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Automatic speech recognition (ASR) and natural language understanding (NLU) systems face high computational costs due to the need to execute multiple machine learning models for each user utterance, leading to inefficiencies and resource wastage, especially when only a small number of skills are applicable to a given input.
Innovation Solution
A cache-based skill selector is implemented to store historical skills used for specific utterances, allowing for retrieval from cache instead of re-executing extensive NLU processes, thereby reducing the number of skill-specific models that need to be executed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple machine learning models are executed for each user utterance to ensure accurate skill selection, then the reliability of skill selection is improved, but the computational cost and processing time increase significantly
Solution Approach 1:
The system performs preliminary action by caching skill identifiers based on historical utterance data before actual skill selection is needed. When an utterance is received, the system first checks the cache for pre-computed skill identifiers, avoiding the need to execute multiple machine learning models in real-time. This preliminary caching of results resolves the contradiction by providing fast retrieval (reducing processing time) while maintaining accurate skill selection (reliability) through pre-validated cached data.
2Reliability
If multiple machine learning models are executed for each user utterance to ensure accurate skill selection, then the reliability of skill selection is improved, but the computational resources consumed increase
Solution Approach 1:
The system performs preliminary computation of skill identifiers and caches them based on historical utterance patterns. This shifts the computational energy consumption from real-time processing to offline batch processing, significantly reducing the energy required during actual user interactions while maintaining reliable skill selection through pre-validated cached results.
Solution Approach 2:
Instead of executing multiple machine learning models for each utterance, the system creates and stores simplified copies (skill identifiers) of the model outputs in a cache. These cached skill identifiers serve as lightweight representations that can be retrieved instantly without re-running the computationally intensive models, thereby reducing energy consumption while preserving selection accuracy.
3Adaptability or versatility
If extensive NLU processes are executed for each utterance to identify applicable skills, then the adaptability of the system is improved, but the device complexity increases
Solution Approach 1:
The system extracts only the essential skill identifier information from the full NLU processing results and stores it in the cache. This extraction approach maintains the adaptability to identify multiple skills while simplifying the stored data structure to just skill identifiers, thereby reducing system complexity without sacrificing the ability to handle diverse utterances and skill types.
Data Source
AI summary
Devices and techniques are generally described for inference reduction in natural language processing using semantic similarity-based caching. In various examples, first automatic speech recognition (ASR) data representing a first natural language input may be determined. A cache may be searched using the first ASR data. A first skill associated with the first ASR data may be determined from the cache. In some examples, first intent data representing a semantic interpretation of the first natural language input data may be determined by using a first natural language process associated with the first skill.