Hierarchical Virtual Agent Query Classification
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Solution Overview
Problem
Existing virtual agent systems face challenges in accurately processing user queries with customer-specific intents not covered by pre-trained classifiers, leading to suboptimal performance and requiring significant migration efforts when updates are made to the pre-trained classifiers.
Innovation Solution
Implementing a hierarchical classification system using two independent classifiers: a customer-specific classifier for customer-specific intents and a pre-trained classifier for non-customer-specific intents, allowing for out-of-domain detection and decoupling customer-specific optimizations from service provider updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a pre-trained classifier is used for all customers, then the system is simple to deploy and maintain, but it cannot accurately classify customer-specific user intents
Solution Approach 1:
The classification system is segmented into two independent classifiers: a pre-trained classifier for general user intents and a customer-specific classifier for domain-specific intents. This segmentation allows each classifier to specialize in particular intent types, improving overall classification accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
An intermediary component is introduced that receives the user query and intelligently routes it to the appropriate classifier based on the detected intent domain. This mediator layer coordinates between the pre-trained and customer-specific classifiers, enabling accurate classification without requiring a single complex unified system.
2Reliability
If a customer-specific classifier is added to handle domain-specific intents, then classification accuracy improves, but the system becomes more complex and harder to maintain
Solution Approach 1:
By segmenting the classification functionality into separate pre-trained and customer-specific classifiers, the system maintains operational simplicity for each component while achieving comprehensive coverage. Each classifier can be independently trained, deployed, and maintained, reducing the operational burden compared to a monolithic system.
Solution Approach 2:
The pre-trained classifier serves as a universal component that handles general user intents across all customers, while the customer-specific classifier handles domain-specific intents. This multi-functionality approach allows the system to address diverse classification needs without proportionally increasing maintenance complexity.
3Reliability
If the pre-trained classifier is updated to improve general intent recognition, then overall system performance improves, but customer-specific optimizations are lost or require retraining
Solution Approach 1:
The independent segmentation of classifiers allows the pre-trained classifier to be updated and retrained without affecting the customer-specific classifier. This separation eliminates the need to retrain customer-specific models when general intent recognition is improved, saving significant time and preserving customer-specific optimizations.
Solution Approach 2:
The customer-specific classifier is trained in advance on customer-specific data and stored for later use. This preliminary action ensures that when the pre-trained classifier is updated, the customer-specific classification capability is already prepared and can be immediately applied without additional retraining time.
4Device complexity
If a single unified classifier is used, then the system architecture is simple, but it requires significant migration efforts when updates are made
Solution Approach 1:
The segmented classifier architecture, while slightly more complex than a single unified classifier, enables independent updates of each component. This modularity significantly eases system updates by allowing the pre-trained classifier to be updated without affecting customer-specific deployments, eliminating significant migration efforts.
Data Source
AI summary
A method of processing user queries for a virtual agent is provided. Data representing a user query directed to a virtual agent is received. It is determined whether the user query corresponds to any one of a first plurality of predefined user intents, by processing the received data using a first classifier configured to classify user queries according to the first plurality of user intents. In response to a positive determination, first output data is generated based on a user intent of the first plurality of user intents to which the user query is determined to correspond. In response to a negative determination, the received data is processed using a second classifier to generate second output data, the second classifier being configured to classify user queries according to a second plurality of predefined user intents. A predetermined action is performed based on either the first or second output data.