Spoken Language Model Adaptation via Active Learning
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Solution Overview
Problem
Building natural language spoken dialog systems requires extensive human intervention, including manual transcription, labeling, and design decisions, making the process error-prone, time-consuming, and costly, with limited scalability.
Innovation Solution
A method for adapting an existing classification model using labeled data from a target application, reducing the need for human-labeling effort through techniques like boosting and active learning, which leverages existing models and data to improve intent classification systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual transcription and labeling are used to build spoken dialog systems, then system performance reaches a useful level, but the process becomes time-consuming and costly
Solution Approach 1:
The patent applies preliminary action by using pre-trained language models and existing labeled data from related domains before building the target spoken dialog system. This allows the system to start with a head start, reducing the amount of new labeling required. The adaptation process leverages pre-existing knowledge to accelerate development without sacrificing performance.
Solution Approach 2:
The patent changes parameters by adapting model weights and configurations from a source domain to a target domain through supervised fine-tuning. Instead of training from scratch, the system modifies existing model parameters using a smaller labeled dataset, significantly reducing the time and cost while maintaining or improving performance through domain-specific adaptation.
2Reliability
If extensive human intervention is used for system design and deployment, then system functionalities are properly defined, but scalability is compromised
Solution Approach 1:
The patent applies self-service by enabling the system to automatically adapt to new domains using the adaptation framework. Instead of requiring human experts to manually redesign systems for each new application, the system performs self-adaptation through supervised fine-tuning on domain-specific data, allowing scalable deployment across multiple domains with minimal human intervention.
Solution Approach 2:
The patent achieves universality by creating a domain-adapted language model that can serve multiple spoken dialog applications. The adapted model maintains core language understanding capabilities while incorporating domain-specific knowledge, allowing it to function across different domains and applications without requiring separate models for each use case.
3Manufacturing precision
If UE experts make non-trivial design decisions through several design cycles, then system core functionalities are optimized, but the process becomes error-prone and costly
Solution Approach 1:
The patent substitutes the mechanical process of manual design iteration with an automated adaptation process. Instead of UE experts manually adjusting system parameters through multiple design cycles, the system automatically optimizes itself through supervised fine-tuning on labeled data, replacing human mechanical adjustment with algorithmic optimization that is more precise and less error-prone.
Solution Approach 2:
The patent implements feedback through the supervised adaptation process, where the system learns from labeled data and continuously improves its performance. The feedback loop allows the model to automatically adjust its parameters based on performance metrics, reducing the need for manual design cycles and expert intervention while achieving optimized functionality.
4Measurement precision
If large amounts of labeled data are collected for target application, then classification model performance improves, but the amount of human-labeling effort increases
Solution Approach 1:
The patent applies partial action by using a smaller, carefully selected labeled dataset for adaptation rather than requiring large amounts of labeled data. The supervised fine-tuning process achieves effective domain adaptation with limited labeled examples, leveraging the pre-trained model's existing knowledge to reduce the labeling burden while maintaining or improving classification performance.
Solution Approach 2:
The patent uses preliminary action by pre-training the language model on large corpora before domain adaptation. This preliminary training provides a strong foundation that reduces the amount of domain-specific labeled data needed, as the model already possesses general language understanding capabilities that transfer to the target domain with minimal additional labeling.
Data Source
AI summary
An apparatus and a method are provided for building a spoken language understanding model. Labeled data may be obtained for a target application. A new classification model may be formed for use with the target application by using the labeled data for adaptation of an existing classification model. In some implementations, the existing classification model may be used to determine the most informative examples to label.