Domain-Aware Vector Translation for Precise NLU Intent Recognition
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Solution Overview
Problem
Existing natural language understanding (NLU) systems struggle to derive meaningful insights from complex natural language utterances, particularly when domain-specific terminology is involved, leading to suboptimal performance and adaptability across different domains.
Innovation Solution
A domain-aware vector encoding (DAVE) framework that translates domain-agnostic semantic vectors into domain-aware vectors, enhancing the NLU system's ability to recognize intent and entities by leveraging a trained vector translator model, while ensuring performance and resource constraints are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If domain-agnostic semantic vectors are used in NLU systems, then the system can process general language, but it fails to accurately understand domain-specific terminology and concepts
Solution Approach 1:
The patent introduces a domain adaptation layer as an intermediary component between the pre-trained language model and the NLU system. This layer contains domain-specific knowledge and acts as a bridge to translate general semantic vectors into domain-aware representations, thereby resolving the contradiction between handling general language and understanding domain-specific terminology.
Solution Approach 2:
The patent modifies the semantic vector representations by applying domain-specific transformations. The domain adaptation layer adjusts the parameters of the semantic vectors to reflect domain-specific meanings and relationships, enabling the system to maintain general language processing capabilities while achieving precision in domain-specific contexts.
2Measurement precision
If complex NLU models are used to improve understanding of complex utterances, then recognition accuracy improves, but computational resource consumption increases
Solution Approach 1:
The patent applies domain adaptation transformations in advance, before the main NLU processing pipeline. By pre-processing the semantic vectors with domain-specific knowledge, the system reduces the computational burden on subsequent processing stages while maintaining or improving recognition accuracy.
Solution Approach 2:
The patent creates enhanced semantic representations by copying and transforming the original semantic vectors through the domain adaptation layer. This allows the system to work with enriched representations that improve accuracy without requiring complete re-architecting of the NLU model.
3Reliability
If the NLU system is customized for specific domains, then domain performance improves, but system complexity and training requirements increase
Solution Approach 1:
The patent segments the NLU system into distinct functional components: a general language processing backbone and a separate domain adaptation layer. This segmentation allows the domain-specific functionality to be added as a modular component without fundamentally altering the core system architecture, thereby managing complexity while improving domain performance.
Solution Approach 2:
The domain adaptation layer is designed to be universal across different domains. It can be configured and trained for various domains without requiring complete system redesign, enabling the same base architecture to serve multiple domains with different specialized requirements.
Data Source
AI summary
A natural language understanding (NLU) framework includes a domain-aware vector encoding (DAVE) framework. The DAVE framework enables a designer to create a DAVE system having a domain-agnostic semantic (DAS) model and a corresponding trained vector translator (VT) model. The DAVE system uses the DAS model to generate domain-agnostic semantic vectors for portions of a user utterance, and then uses the VT model to translate the domain-agnostic semantic vectors into a domain-aware semantic vectors to be used by a NLU system of the NLU framework during a meaning search operation. The VT model is also designed to provide predicted intent classifications for the portions the user utterance. Both the NLU system and the DAVE system of the NLU framework are highly configurable and refer to various NLU constraints during operation, including performance constraints and resource constraints provided by a designer or user of the NLU framework.