Domain-Adapted Intent Classification Using Orthogonal Transformation
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Solution Overview
Problem
Machine learning models trained on generic datasets struggle to accurately classify domain-specific natural language expressions, leading to poor performance in production due to variations such as user errors or slang usage.
Innovation Solution
A system performs domain adaptation of a base machine learning model by applying an orthogonal transformation to a domain-specific training dataset, reducing dimensions and minimizing intra-class distances while maximizing inter-class distances, resulting in a domain-specific model that accurately classifies intents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a machine learning model is trained using generic training data, then the model can be developed quickly and with less domain-specific data, but the model performs poorly in production when encountering domain-specific variations such as user errors or slang
Solution Approach 1:
The system performs preliminary domain adaptation by applying orthogonal transformation to the base model using domain-specific training data before deployment. This preliminary action transforms the generic model into a domain-specific model that is better prepared to handle domain variations, thus improving reliability without requiring complete retraining from scratch
Solution Approach 2:
The system changes the parameters of the base model by applying orthogonal transformation, which modifies the model's weight matrices to adapt to domain-specific characteristics. This parameter transformation allows the model to maintain good performance on generic data while becoming specialized for domain-specific variations
2Reliability
If domain-specific training data is collected and used to retrain the model, then classification accuracy improves, but the time and resources required for model development and training increase
Solution Approach 1:
The orthogonal transformation is performed as a preliminary step that efficiently adapts the base model to domain-specific data. This preliminary transformation achieves domain adaptation faster than complete retraining, reducing the time loss while maintaining improved classification accuracy
Solution Approach 2:
Instead of creating a completely new model through extensive retraining, the system creates a transformed copy of the base model using orthogonal transformation. This copying approach with transformation leverages the existing model structure while adapting it to domain specifics, significantly reducing training time compared to training from scratch
3Device complexity
If the base model is used without transformation, then the model structure remains simple and interpretable, but the model cannot accurately classify domain-specific expressions
Solution Approach 1:
The system applies orthogonal transformation to modify the model parameters while maintaining the overall model structure. This parameter change approach improves measurement precision for domain-specific classification without fundamentally changing the model architecture, thus balancing complexity and precision
Data Source
AI summary
The system receives a base machine learning model trained using a generic dataset. For example, the base machine learning model may be an off-the-shelf machine learning based model. The base machine learning model is trained to receive an input and generate a feature vector representing the input. The input may be a natural language expression, an image, or any other type of input. The system receives a domain specific training dataset based on known categories for input values. The system determines an orthogonal transformation for reducing the dimensions of the base machine learning model using on the domain specific training dataset. The system applies the orthogonal transformation to the base machine learning model to obtain a domain specific machine learning model. The system uses the domain specific machine learning model for processing inputs, for example, in a production environment.