Delayed Response Selection in Input Understanding Systems
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Solution Overview
Problem
Conversational understanding systems face errors due to unrecoverable mistakes at various stages of processing, such as input recognition and belief state processing, leading to cascading errors and combinatorial explosion, which affects accuracy and efficiency.
Innovation Solution
The system generates and propagates a ranked list of alternatives throughout processing, delaying the selection of top choices until final operations, applying additional context and knowledge data to minimize errors and avoid combinatorial explosion by using configurable intermediate rankers to prune alternative lists.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system processes all alternative hypotheses through complete processing pipelines, then accuracy is improved, but computational complexity and processing time increase exponentially
Solution Approach 1:
The processing system is divided into multiple stages: initial hypothesis generation, intermediate filtering with confidence scoring, selective expansion of promising alternatives, and final evaluation. This segmentation allows the system to process only relevant alternatives in detail while performing lightweight filtering on others, reducing computational complexity from exponential to manageable levels while maintaining accuracy through multi-stage verification
Solution Approach 2:
The system performs partial processing on all alternatives (initial filtering and confidence scoring) and excessive action only on promising alternatives (full processing pipeline). By applying complete processing selectively rather than universally, the system achieves high accuracy for critical decisions while avoiding the computational burden of processing all possible alternatives equally
2Reliability
If the system retains and processes all alternative hypotheses, then error recovery capability is improved, but memory usage and processing overhead increase
Solution Approach 1:
The system applies different retention qualities to different alternatives based on their confidence scores and relevance. High-confidence alternatives receive full retention and detailed processing, while low-confidence alternatives are pruned or retained only in compressed form. This local differentiation of quality allows error recovery capability for important alternatives while minimizing memory usage through selective retention
Solution Approach 2:
The system discards low-confidence alternative hypotheses early in the processing pipeline to reduce memory usage, but retains the ability to recover and reprocess them if errors are detected in the primary interpretation. This discarding and recovering mechanism enables error recovery capability without permanently storing all possible alternatives, balancing memory constraints with reliability requirements
3Reliability
If the system delays response selection until final operations, then decision accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary filtering, confidence scoring, and ranking of alternative hypotheses before final response selection. By preparing and organizing alternatives in advance with confidence metrics, the system enables delayed selection without linearly increasing processing time, as the preliminary work is done in parallel during the processing pipeline rather than sequentially after all processing is complete
Data Source
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AI summary
Examples of the present disclosure describe processing by an input understanding system/service. A received input is processed to generate a set of alternatives for recognizing the received input. The set of alternatives is filtered. Filtering comprises ranking the set of alternatives and propagating a plurality of the ranked alternatives for additional processing. The propagated alternatives are processed to generate an expanded set of alternatives for potential hypotheses based on the received input. The expanded set of alternatives is filtered. Filtering comprises ranking alternatives of the expanded set and propagating a plurality of the ranked alternatives of the expanded set for additional processing. The propagated alternatives of the expanded set are evaluated based on application of knowledge data fetched from external resources. A response to the received input is generated. Generating of the response comprises ranking the evaluated alternatives and selecting the response based on the ranked and evaluated alternatives.