Multi-Task Two-Tower Networks for Reliable Guideline Evaluation
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Solution Overview
Problem
Existing machine learning models for evaluating content against multiple guidelines are unreliable due to a lack of knowledge transfer, requiring separate training for each guideline and making it difficult to onboard new guidelines, and are limited in handling text and image content.
Innovation Solution
A multi-task two-tower neural network that embeds content into a shared latent n-dimensional space, allowing guideline-specific layers to share knowledge and learn from each other, facilitating easier addition of new guidelines and evaluating both text and image content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate machine learning models are trained for each guideline, then each guideline can be evaluated independently, but the reliability of each model is reduced due to lack of knowledge transfer and it becomes difficult to onboard new guidelines
Solution Approach 1:
The patent combines multiple separate guideline evaluation models into a single multi-task neural network that simultaneously evaluates content against multiple guidelines. This shared model architecture enables knowledge transfer across different guideline evaluations, improving the reliability of each evaluation while reducing the overall system complexity of maintaining separate models.
Solution Approach 2:
The neural network is designed with universal components that serve multiple functions - the same model structure and shared layers are used to evaluate different types of guidelines (e.g., violent content, sexual content, spam). This multi-functional design allows the system to handle various guideline types with a single model, improving reliability through knowledge sharing while simplifying the addition of new guidelines.
2Adaptability or versatility
If separate models are used for each guideline type, then each guideline can be evaluated, but it requires separate training for each guideline and makes it difficult to onboard new guidelines
Solution Approach 1:
The patent implements pre-training of the neural network on large datasets before fine-tuning for specific guidelines. This preliminary training establishes a strong foundation that can be rapidly adapted to new guidelines through transfer learning, significantly reducing the training time required for onboarding new guideline types while maintaining high adaptability across different evaluation tasks.
Solution Approach 2:
The system uses parameter sharing and selective fine-tuning strategies where most model parameters remain fixed when adding new guidelines, and only specific parameters are updated during training. This approach enables rapid adaptation to new guideline types by changing only the necessary parameters rather than retraining the entire model, thus reducing training time while preserving versatility.
3Adaptability or versatility
If traditional machine learning models are used, then content evaluation can be performed, but they are limited in handling both text and image content effectively
Solution Approach 1:
The patent introduces specialized intermediary components including CNN layers for image processing and attention mechanisms that bridge text and image modalities. These intermediaries effectively process different content types and integrate their representations, enabling the model to handle both text and image content while maintaining high evaluation accuracy through coordinated multi-modal analysis.
Data Source
AI summary
In an example embodiment, a solution is provided where a multi-task two-tower machine learning model is utilized to evaluate content against multiple different guidelines. Each of these guidelines represent a different task in the single multi-task two-tower machine learning model, and knowledge can be shared between the layers of the model devoted to each guideline, eliminating the need for separately trained models and also greatly improving the reliability of the predictions. The multi-task two-tower machine learning model is also capable of evaluating both text content and image content, via a respective tower utilized to embed such content into a shared latent n-dimensional space.