Natural Language Processing with Progressive Context Refinement
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Solution Overview
Problem
Existing spoken language understanding systems face challenges in reducing latency and improving accuracy of responses by effectively utilizing context information as it becomes available over time.
Innovation Solution
A system processes user inputs through multiple iterations, leveraging a language model and contextual information groups to generate task predictions, using a large language model (LLM) and knowledge graphs to enhance accuracy and reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system waits for all context information to be available before processing, then accuracy of task predictions is improved, but latency increases
Solution Approach 1:
The context information is segmented into multiple groups that become available at different times. The system processes predictions in stages: first using initially available context, then refining with additional context groups as they become available, rather than waiting for all context to be complete before any processing occurs.
Solution Approach 2:
The system performs preliminary processing with the context information that is available at each moment. Task predictions are generated and refined in advance as context becomes available, allowing the system to start working with partial information and improve predictions over time rather than waiting for complete context.
2Measurement precision
If the system processes user inputs through multiple iterations with all available context, then accuracy of outputs is improved, but computational complexity increases
Solution Approach 1:
The computational process is segmented into multiple iterations, where each iteration processes a subset of context groups. This breaks down the complex task of processing all context at once into manageable stages, reducing the computational burden at each step while still achieving high accuracy through cumulative processing.
Solution Approach 2:
The system dynamically adjusts the amount of context processed in each iteration based on what is available. The computational complexity varies over time as different context groups become available, allowing the system to optimize resource usage while maintaining accuracy through progressive refinement.
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.