State-Dependent Utterance Classifiers for Spoken Dialog Systems
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Solution Overview
Problem
Spoken dialog systems face performance issues due to semantic classifiers being built without adequate data, spanning multiple contexts, or using outdated data, leading to suboptimal performance and out-of-scope utterances, especially when callers interact with systems designed to emulate human agents.
Innovation Solution
A system and method for generating state-dependent semantic classifiers using state-vector variables, where utterances are matched to specific subsets, and classifiers are trained and tested to optimize performance for individual states, allowing for the selection of the best-performing classifiers and elimination of worse ones, thereby improving classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If semantic classifiers are built to span several contexts, then the classifier can handle multiple dialog situations, but performance suffers because callers actually behave differently given the context
Solution Approach 1:
The patent divides a single semantic classifier into multiple state-dependent classifiers, each tailored to specific dialog states. By segmenting the classifier based on dialog state variables (such as intent, entities, and conversation flow), the system achieves both versatility (handling multiple contexts) and reliability (accurate classification within each state).
Solution Approach 2:
The patent implements dynamic classifier selection based on real-time dialog state. The system adapts which classifier to use depending on the current dialog state, making the classification process dynamic rather than static. This allows the system to switch between different classifiers optimized for different contexts, maintaining high accuracy across diverse dialog situations.
2Ease of manufacture
If semantic classifiers are built with rules based on designer expectations, then the system can operate without collected data, but performance suffers due to mismatch between expected and actual caller behavior
Solution Approach 1:
The patent employs preliminary action by pre-training state-dependent classifiers on collected dialog data before deployment. The system gathers actual caller utterances, annotates them with dialog states, and trains classifiers in advance on this real data. This preliminary training ensures classifiers learn actual caller behavior patterns rather than relying solely on designer expectations, improving accuracy while maintaining ease of deployment.
Solution Approach 2:
The system implements feedback loops where classifier performance is continuously monitored and used to refine future classifier training. Actual classification results and caller responses provide feedback that informs subsequent training iterations, allowing the system to adapt to evolving caller behaviors and improve accuracy over time while maintaining operational simplicity.
3Productivity
If semantic classifiers are built on small amounts of data, then the development process is faster, but performance suffers due to insufficient training data
Solution Approach 1:
The patent segments the training data and classifiers by dialog state, allowing efficient use of limited data. By dividing the classification task into state-specific subtasks, the system can achieve better performance with smaller amounts of state-specific training data rather than requiring large amounts of general data for a single monolithic classifier.
Solution Approach 2:
The system merges multiple state-dependent classifiers into a unified classification framework. By combining the results of several specialized classifiers (each trained on smaller state-specific datasets), the system achieves high overall accuracy without requiring any single classifier to be trained on large amounts of data, thus maintaining fast development speed.
4Productivity
If semantic classifiers are built on old or unrepresentative data, then the system can deploy classifiers quickly, but performance suffers due to outdated caller behavior patterns
Solution Approach 1:
The patent implements preliminary action by continuously collecting and updating training data from actual system operations. Before deploying updated classifiers, the system pre-trains them on freshly collected, representative data that reflects current caller behaviors. This ensures classifiers are always based on up-to-date patterns while maintaining a structured deployment process that preserves productivity.
Solution Approach 2:
The system performs self-service by automatically collecting, annotating, and using its own operational data to retrain and update classifiers. This self-updating mechanism ensures classifiers continuously adapt to current caller behaviors without requiring manual intervention or deployment of external data, maintaining both accuracy and deployment efficiency.
Data Source
AI summary
A system and a method to generate statistical utterance classifiers optimized for the individual states of a spoken dialog system is disclosed. The system and method make use of large databases of transcribed and annotated utterances from calls collected in a dialog system in production and log data reporting the association between the state of the system at the moment when the utterances were recorded and the utterance. From the system state, being a vector of multiple system variables, subsets of these variables, certain variable ranges, quantized variable values, etc. can be extracted to produce a multitude of distinct utterance subsets matching every possible system state. For each of these subset and variable combinations, statistical classifiers can be trained, tuned, and tested, and the classifiers can be stored together with the performance results and the state subset and variable combination. Once the set of classifiers and stored results have been put into a production system, for a given system state, the classifiers resulting in optimum performance can be selected from the result list and used to perform utterance classification.