Adaptive Dialog System Dynamic n-Best Selection
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Solution Overview
Problem
Conventional dialog systems rely on static rules and data structures, limiting their ability to adapt to varying user queries and responses, and require wasteful retraining on data they already understand, rather than focusing on new information.
Innovation Solution
The implementation of an adaptive dialog system (ADS) that dynamically selects the number of best outputs (n*-best selection) based on the classifier's output for each utterance, allowing for built-in adaptive components to be easily updated with new data and storing misunderstood utterances for later retraining, thereby improving coverage and reducing unnecessary training efforts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed number of candidates (n-best) is used in conventional dialog systems, then the system structure is simple and easy to implement, but the system cannot adapt to varying user queries and has limited coverage
Solution Approach 1:
The patent applies the dynamics principle by transforming the static fixed n-best selection into a dynamic adaptive selection process. The system now determines the number of candidates to select based on the actual distribution of classifier probabilities for each query, rather than using a predetermined fixed number. This allows the system to adapt its behavior to the specific characteristics of each user query, improving versatility while maintaining reasonable complexity through algorithmic automation.
Solution Approach 2:
The patent implements parameter changes by making the number of selected candidates (n) a variable parameter rather than a fixed constant. The system dynamically adjusts this parameter based on the classifier output distribution, selecting more candidates when probabilities are closely distributed and fewer candidates when one option clearly dominates. This parameter adaptation resolves the contradiction by enabling flexibility in handling diverse queries without requiring complete structural redesign.
2Reliability
If the system re-trains on all collected data, then the system learns from all available information, but the system wastes computational resources training on data it already understands
Solution Approach 1:
The patent applies the extraction principle by separating high-confidence data (which the system already understands) from low-confidence data (which requires learning). The system extracts only the low-confidence examples for retraining, effectively taking out the useful subset of data while leaving behind the redundant high-confidence examples. This dramatically reduces computational resource waste while maintaining learning effectiveness, as the system focuses its training efforts only where improvement is needed.
Solution Approach 2:
The patent implements partial action by performing retraining only on a subset of the collected data rather than processing all data. Specifically, it applies partial retraining only to examples where the classifier confidence falls below a threshold, performing exactly the amount of training needed without excessive computation on already-understood examples. This resolves the contradiction by achieving sufficient learning effectiveness through targeted partial action rather than exhaustive full-data retraining.
3Measurement precision
If a high confidence threshold is used for selection, then the system selects only high-confidence outputs improving accuracy, but the system reduces coverage by excluding valid but lower-confidence results
Solution Approach 1:
The patent applies dynamics by making the confidence threshold adaptive rather than fixed. The system dynamically adjusts the effective threshold based on the distribution of confidence scores for each query. When confidence scores are closely distributed, the system effectively lowers the threshold by selecting more candidates. When one candidate clearly dominates, the system effectively raises the threshold by selecting fewer candidates. This dynamic adaptation resolves the contradiction by maintaining high accuracy when possible while expanding coverage when needed.
Solution Approach 2:
The patent implements parameter changes by transforming the static confidence threshold into a dynamic selection criterion based on probability distribution analysis. Rather than applying a fixed threshold that either includes or excludes candidates rigidly, the system changes the selection parameter (number of candidates) based on the relative differences between confidence scores. This allows the system to achieve both high accuracy and broad coverage by adapting the effective threshold behavior to each query's characteristics.
Data Source
AI summary
An optimal selection or decision strategy is described through an example that includes use in dialog systems. The selection strategy or method includes receiving multiple predictions and multiple probabilities. The received predictions predict the content of a received input and each of the probabilities corresponds to one of the predictions. In an example dialog system, the received input includes an utterance. The selection method includes dynamically selecting a set of predictions from the received predictions by generating ranked predictions. The ranked predictions are generated by ordering the plurality of predictions according to descending probability.