Domain Expert Ensemble for NLU Adaptability
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Solution Overview
Problem
Current natural language understanding (NLU) systems face challenges in efficiently adding new domains with limited training data, as they often require retraining a global model from scratch, which is time-consuming and less accurate, and fail to utilize existing resources from other domains effectively.
Innovation Solution
The system employs an ensemble of domain experts that utilize domain attention to add a new domain with limited training data, allowing it to leverage existing resources while maintaining separate domains, by receiving queries from existing domain experts, assigning weights to their answers, and training the new domain expert using these weighted answers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a global model is retrained from scratch to add a new domain, then the system can incorporate the new domain, but the training time increases significantly and accuracy decreases
Solution Approach 1:
The system segments the monolithic global model into multiple domain-specific expert models, each trained on specific domain data. When adding a new domain, only the new domain expert needs to be trained rather than retraining the entire global model, significantly reducing training time while maintaining the ability to handle multiple domains.
Solution Approach 2:
The system merges multiple domain expert models into an ensemble that collaborates to handle various domains. The ensemble combines predictions from individual domain experts, allowing the system to incorporate new domains by adding new experts to the ensemble without affecting existing ones, thus avoiding full retraining.
2Adaptability or versatility
If a global model is retrained from scratch to add a new domain, then the system can incorporate the new domain, but the development resources and costs increase
Solution Approach 1:
By segmenting the model into independent domain experts, the system enables parallel development and training of individual domain models. This reduces development resources required for each domain addition and allows incremental updates without costly full-system retraining.
Solution Approach 2:
Domain experts are pre-trained on their respective domain data beforehand. When a new domain needs to be added, the system can quickly train a new expert using pre-prepared training data and integration mechanisms, reducing the overall development time and resource requirements compared to training a complete global model from scratch.
3Adaptability or versatility
If domain-specific models are used for each domain, then the system can maintain separate domains, but the system complexity increases
Solution Approach 1:
The system merges multiple independent domain expert models into a unified ensemble framework with a common input-output interface. This maintains domain separation for specialized processing while presenting a simplified unified interface to users, effectively managing complexity through modular architecture with standardized integration points.
Solution Approach 2:
The ensemble framework provides universal functionality for handling multiple domains through a standardized architecture. Each domain expert follows the same interface conventions, allowing the system to manage complexity through consistent, multi-functional design patterns that work across all domains.
4Productivity
If existing domain resources are utilized for training a new domain, then the training efficiency improves, but the risk of domain confusion increases
Solution Approach 1:
The system segments learning resources by creating domain-specific expert models that process domain-specific training data. This prevents domain confusion by ensuring each expert learns only from its designated domain's resources, maintaining high domain classification accuracy while allowing efficient utilization of each domain's specific training data.
Solution Approach 2:
The ensemble framework acts as an intermediary that routes inputs to appropriate domain experts based on domain identification. This mediator ensures that training resources are utilized efficiently by each relevant domain expert while preventing domain confusion through proper routing and isolation of domain-specific processing.
Data Source
AI summary
Systems and methods for adding a new domain to a natural language understanding system to form an updated language understanding system with multiple domain experts are provided. More specifically, the systems and methods are able to add a new domain utilizing data from one or more of the domains already present in the natural language understanding system while keeping the new domain and the already present domains separate from each other.