Multi-turn Cross-domain NLU Schema Prediction
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Solution Overview
Problem
Building task/domain-specific natural language understanding (NLU) systems is expensive, time-consuming, and requires significant expertise and resources, limiting the ability of developers to create conversational interfaces.
Innovation Solution
A multi-turn cross-domain NLU system that predicts a schema based on user input and utilizes it to decode responses, allowing for the creation of NLU systems without requiring a pre-defined task or schema, enabling easier, cost-effective, and efficient building of NLU systems for various applications and domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a task/domain-specific NLU system is built using traditional methods, then the system can accurately understand language within that specific domain, but the development process becomes expensive, time-consuming, and requires significant expertise and resources
Solution Approach 1:
The patent applies universality by creating a single NLU system architecture that can handle multiple tasks and domains through schema prediction. Instead of building separate specialized systems for each domain, the system uses a unified model that predicts appropriate schemas based on user input, allowing one system to perform the functions of multiple domain-specific systems. This reduces development complexity while maintaining understanding accuracy across different domains.
Solution Approach 2:
The patent applies preliminary action by pre-training the NLU model on diverse multi-domain data and pre-defining multiple schemas that represent different tasks and domains. The schema prediction component is pre-configured with knowledge about various domains, allowing the system to quickly adapt to new tasks without requiring extensive retraining or manual configuration during deployment.
2Reliability
If traditional NLU system development methods are used, then the system can be optimized for a specific task, but the ability to adapt to new domains and tasks is limited
Solution Approach 1:
The patent applies dynamics by making the NLU system adaptable through schema prediction. The system dynamically selects which schema to apply based on the user input and context, allowing it to transition between different tasks and domains. This dynamic adaptation mechanism enables the system to maintain reliable performance across varying tasks without being locked into a single domain configuration.
Solution Approach 2:
The patent applies parameter changes by modifying the operational parameters of the NLU system through schema selection. Different schemas represent different task configurations and domain knowledge, and the system changes these parameters dynamically based on the predicted schema, enabling adaptation to new domains while maintaining task performance reliability.
3Ease of operation
If developers build custom NLU systems for each application, then the system can be tailored to specific needs, but the resource allocation becomes extensive and costly
Solution Approach 1:
The patent applies universality by creating a single NLU system that can serve multiple applications and domains. The schema prediction mechanism allows the same underlying model to be customized for different tasks without requiring separate systems, thereby reducing resource requirements while maintaining the ability to tailor the system to specific needs through schema selection.
Solution Approach 2:
The patent applies segmentation by separating the NLU system into modular components: the core NLU model, the schema prediction component, and multiple predefined schemas. This segmentation allows developers to customize the system by selecting different schemas for different applications without modifying the core model, reducing resource requirements while maintaining ease of customization.
4Ease of manufacture
If a pre-defined task and schema are selected for NLU system building, then the development process becomes simpler, but the system is limited to that specific task and domain
Solution Approach 1:
The patent applies universality by designing an NLU system that maintains simplicity in development while achieving task versatility through schema prediction. The system uses a unified architecture that can handle multiple tasks without requiring developers to choose a single task definition, thereby combining ease of manufacture with adaptability to various domains and tasks.
Data Source
AI summary
Multi-turn cross-domain natural language understanding (NLU) systems and platforms for building the multi-turn cross-domain NLU system are provided. Further, methods for using and building the multi-turn cross-domain NLU system are provided. More specifically, the multi-turn cross-domain NLU system supports multi-turn bot/agent/application scenarios for new domains without having to select a task definition and/or define a new schema during the building of the NLU system. Accordingly, the platform for building the multi-turn cross-domain NLU system that does not require the builder to select a task and/or build a schema for a selected task provides an easy to use, cost effective, and efficient service for building a NLU system. Further, the multi-turn cross-domain NLU system provides a more versatile NLU system than previously utilized NLU systems that were trained for and limited to a selected task and/or domain.