Endpoint-Dependent Natural Language Understanding System
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Solution Overview
Problem
Developing language understanding (LU) systems that are labor-intensive, costly, and prone to errors due to the need for custom development for specific service points and application domains, leading to inconsistencies and inaccuracies in interpreting user inputs across different endpoint mechanisms.
Innovation Solution
A computer-implemented LU system that includes an interface component, an endpoint-independent subsystem, an endpoint-dependent subsystem, and a ranking component, which interprets linguistic items in a manner that accounts for the specific endpoint mechanism, enabling flexible and scalable development and maintenance by separating endpoint-independent and endpoint-dependent interpretations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a custom LU system is developed for each specific service point and application domain, then interpretation accuracy for that specific domain is improved, but system complexity and development cost increase significantly
Solution Approach 1:
The LU system is segmented into multiple independent interpreter components, each specialized for a specific domain or application scenario. These components work in parallel and their results are combined through a ranking mechanism. This allows the system to achieve high interpretation accuracy for specific domains while avoiding the complexity of a single monolithic custom system, as each component can be developed and maintained independently.
2Measurement precision
If multiple custom LU systems are developed for different endpoint mechanisms, then endpoint-specific interpretation accuracy is improved, but maintenance effort and time increase
Solution Approach 1:
The system employs a universal architecture where a single LU system serves multiple endpoint mechanisms through a collection of domain-specific interpreter components. Each interpreter component is designed to be domain-specific rather than endpoint-specific, allowing the same set of interpreters to handle linguistic items from various endpoints. This universal approach enables the system to maintain endpoint-specific interpretation accuracy while significantly reducing maintenance time, as updates and improvements need to be made only once rather than replicated across multiple endpoint-specific systems.
3Device complexity
If a single unified LU system is used across all endpoint mechanisms, then system simplicity is improved, but interpretation accuracy for endpoint-specific contexts deteriorates
Solution Approach 1:
The unified LU system is segmented into multiple specialized interpreter components, each optimized for specific domains or application scenarios. This segmentation allows the system to maintain simplicity at the architectural level while achieving high interpretation accuracy through the collective expertise of multiple specialized components. The ranking component integrates results from these segments to provide accurate endpoint-specific interpretations within a unified framework.
Solution Approach 2:
Different interpreter components within the unified system possess different local qualities or specializations tailored to specific domains or application scenarios. Each interpreter is optimized for its particular domain, providing high interpretation accuracy for that specific context. The system as a whole maintains simplicity while incorporating these local quality differences through the modular interpreter architecture and ranking mechanism.
4Measurement precision
If custom LU systems are developed for each application domain, then domain-specific interpretation accuracy is improved, but development cost and labor increase
Solution Approach 1:
The system uses a universal architecture where a single set of domain-specific interpreter components serves multiple application domains and endpoint mechanisms. This universal approach allows the system to achieve domain-specific interpretation accuracy while reducing development cost, as the interpreter components can be reused across multiple applications rather than developing separate custom systems for each domain.
Solution Approach 2:
The development effort is segmented into independent interpreter components that can be developed, tested, and validated separately. This segmentation reduces overall development cost by allowing parallel development and reusing common infrastructure, while still achieving domain-specific accuracy through the specialized nature of each interpreter component.
Data Source
AI summary
A computer-implemented technique is described for processing a linguistic item (e.g., a query) in an efficient and scalable manner. The technique interprets the linguistic item using a language understanding (LU) system in a manner that is based on a particular endpoint mechanism from which the linguistic item originated. The LU system may include an endpoint-independent subsystem, an endpoint-dependent subsystem, and a ranking component. The endpoint-independent subsystem interprets the linguistic item in a manner that is independent of the particular endpoint mechanism. The endpoint-dependent subsystem interprets the linguistic item in a manner that is dependent on the particular endpoint mechanism. The ranking component generates final interpretation results based on intermediate results generated by the endpoint-independent subsystem and the endpoint-dependent subsystem, e.g., by identifying the most likely interpretation of the linguistic item.