Adaptive Dialog System Dynamic Candidate 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, lacking comprehensive coverage and efficiency.
Innovation Solution
The implementation of an adaptive dialog system (ADS) that dynamically selects the number of output candidates (n) based on the classifier's probability distribution for each utterance, allowing for dynamic storage and retraining of misunderstood utterances, thereby improving coverage and reducing unnecessary training efforts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed number of output candidates (n) is selected in traditional n-best methods, then the system operation is simple and deterministic, but the system cannot adapt to varying query complexities and wastes computational resources on unnecessary training
Solution Approach 1:
The patent implements dynamic selection of the number of output candidates (n) based on the accumulated probability distribution of the statistical classifier. Instead of using a fixed n, the system dynamically determines n by evaluating the probability distribution and selecting the number of candidates that achieve a target accumulated probability threshold. This allows the system to adapt to varying query complexities automatically, resolving the contradiction between adaptability and operational simplicity.
Solution Approach 2:
The patent changes the parameter n (number of output candidates) from a static fixed value to a dynamic variable determined by the probability distribution analysis. By changing this parameter based on the accumulated probability of candidates, the system achieves adaptability to different query scenarios without requiring complex manual configuration or intervention.
2Productivity
If the system retrains on all collected data, then comprehensive training coverage is achieved, but unnecessary retraining on already-understood data wastes computational resources and time
Solution Approach 1:
The patent extracts and identifies high-confidence utterances from the collected data based on the accumulated probability threshold. These high-confidence utterances are separated from the training dataset, allowing the system to retrain only on the remaining low-confidence or misunderstood utterances. This extraction approach improves training efficiency by eliminating redundant training on already-understood data while preserving all useful training information.
Solution Approach 2:
Instead of performing full retraining on all collected data (excessive action), the patent applies partial retraining only on the subset of data that requires it (utterances below the confidence threshold). This partial action approach optimizes training efficiency by avoiding unnecessary computational resources spent on retraining data that the system already understands confidently.
3Adaptability or versatility
If the dialog system uses static rules and data structures, then system implementation is straightforward and reliable, but the system cannot comprehensively cover all possible user queries
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously evaluates the accumulated probability of output candidates and uses this feedback to dynamically adjust the number of candidates selected and to identify utterances requiring retraining. This feedback loop enables the system to adapt to new query patterns over time, improving coverage while maintaining straightforward implementation through automated probability-based decision-making.
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.