Iterative NLP Task Prediction for Low-Latency Voice Responses
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Solution Overview
Problem
Existing spoken language understanding systems face challenges in accurately and efficiently processing user inputs due to limited contextual information, leading to increased latency and potential inaccuracies in response generation.
Innovation Solution
A system that processes user inputs in multiple iterations, utilizing a language model to generate task predictions as contextual information becomes available, incorporating personalized, general, and factual knowledge graphs, and performing iterative processing to enhance accuracy and reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system waits for all contextual information to be available before processing, then response accuracy is improved, but response latency increases
Solution Approach 1:
The system segments the processing into multiple iterations: a first iteration processes available contextual information to generate preliminary task predictions quickly, and subsequent iterations refine these predictions as additional contextual information becomes available. This segmentation allows the system to deliver initial responses faster while maintaining accuracy through iterative refinement.
Solution Approach 2:
The system performs preliminary processing in the first iteration using the contextual information that is available at that time, generating preliminary task predictions before all contextual information is available. This preliminary action reduces initial latency while allowing for later refinement to improve accuracy.
2Measurement precision
If the system processes user inputs in multiple iterations, then response accuracy is improved, but computational complexity increases
Solution Approach 1:
The system performs partial processing in the first iteration using only the contextual information available at that time, rather than waiting to process all possible information at once. This partial action reduces the immediate computational burden while maintaining the option to refine results in subsequent iterations, balancing accuracy with computational complexity.
3Loss of time
If the system uses limited contextual information for processing, then response latency is reduced, but response accuracy deteriorates
Solution Approach 1:
The system segments the processing into multiple iterations where the first iteration uses limited contextual information to provide fast preliminary results, and subsequent iterations incorporate additional contextual information to refine accuracy. This segmentation resolves the contradiction by accepting limited information initially for speed, then improving accuracy progressively.
Solution Approach 2:
The system uses feedback from the first iteration's task predictions to guide subsequent processing. The preliminary predictions serve as feedback that helps prioritize which additional contextual information to process next, allowing the system to improve accuracy efficiently without simply processing all information uniformly.
Data Source
AI summary
Techniques for processing with respect to a user input as contextual information is available are described. A system generates a first task prediction using first context data that is available when a user input is received. The system generates a second task prediction (e.g., updated first task prediction) when second context data is received, and then further generates a third task prediction when third context data is received. Example first context data may include device type information, time information, location, etc. Example second context data may include automatic speech recognition (ASR) data. Example third context data may include natural language understanding (NLU) data. Using the third task prediction, the system generates an output responsive to the user input.