Generic Language Understanding Model Configuration
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Solution Overview
Problem
Natural language understanding (NLU) systems require significant machine learning expertise, substantial labeled data, and are not easily accessible for configuring new models, making it difficult to leverage existing data and models for new applications.
Innovation Solution
The system identifies and collects previously configured schemas for various applications, generates a generic schema, maps the collected schemas, and uses the mapped schema to train models, providing an interface for browsing and selecting models, which can be bundled for implementation on computing devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If statistical data models are used for NLU systems, then model quality can be improved, but the complexity of configuration and expertise required increases significantly
Solution Approach 1:
The patent creates a universal schema that can be used across multiple NLU applications and domains. Instead of requiring separate statistical data models for each application, the system uses a single configurable schema that can be adapted to different domains through configuration files, reducing the need for machine learning expertise while maintaining model quality.
Solution Approach 2:
The system copies and reuses existing schema definitions across multiple applications. Rather than creating new statistical models from scratch for each application, the patent allows copying of proven schemas and adapting them through configuration, significantly reducing the expertise and time required while maintaining reliability.
2Measurement precision
If application-specific labeled data is collected for training models, then model accuracy improves, but the time and resources required for data collection and model configuration increase
Solution Approach 1:
The patent performs preliminary actions by pre-defining schema structures, data formats, and model configurations that can be reused across applications. Configuration files are prepared in advance with template schemas that can be quickly adapted to new applications, eliminating the need for time-consuming data collection and model training from scratch.
Solution Approach 2:
The system allows model adaptation through parameter changes in configuration files rather than requiring complete retraining. By changing parameters such as domain-specific terms, slot definitions, and intent patterns in the schema configuration, the system can adapt to new applications quickly while maintaining the underlying trained model structure.
3Reliability
If multiple separate models are trained for different applications, then each model can be optimized for its specific domain, but the bandwidth and storage resources required increase
Solution Approach 1:
The patent merges multiple application-specific models into a single unified schema-based system. Instead of maintaining separate trained models for each application, the system combines them into one configurable schema that can serve multiple domains, significantly reducing the quantity of data and model files required while maintaining domain-specific performance through configuration.
4Adaptability or versatility
If new NLU models are configured from scratch, then models can be customized for specific needs, but the expertise and resources required make it difficult to leverage existing models
Solution Approach 1:
The patent segments the model configuration into distinct layers: a universal schema structure, domain-specific adaptations, and application-specific customizations. This segmentation allows users to leverage existing schemas while easily customizing specific parts for their needs, making model configuration accessible without requiring deep machine learning expertise.
Solution Approach 2:
The schema acts as an intermediary between existing trained models and new application requirements. Instead of requiring direct customization of complex statistical models, the schema provides a simplified configuration interface that mediates between the underlying model and application-specific needs, enabling easy customization while leveraging existing model capabilities.
Data Source
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AI summary
Examples of the present disclosure describe systems and methods of configuring generic language understanding models. In aspects, one or more previously configured schemas for various applications may be identified and collected. A generic schema may be generated using the collected schemas. The collected schemas may be programmatically mapped to the generic schema. The generic schema may be used to train on ore more models. An interface may be provided to allow browsing the models. The interface may include a configuration mechanism that provides for selecting on or more of the models. The selected models may be bundled programmatically, such that the information and instructions needed to implement the models are configured programmatically. The bundled models may then be provided to a requestor.