Language Model Granularity Selection via Hierarchical Class Evaluation
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Solution Overview
Problem
Existing language models face challenges in accurately representing concepts at different levels of granularity, leading to suboptimal performance in speech recognition systems, as they often rely on fixed classes without evaluating the best combination of classes for specific tasks.
Innovation Solution
A method is introduced to generate and evaluate language models using different classes from a concept's hierarchy, selecting the most appropriate classes based on performance metrics like word error rate and perplexity, allowing for the determination of the optimal level of granularity for concept representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a language model uses a broad top-level class to represent a concept, then the model structure is simpler and more general, but the modeling accuracy decreases
Solution Approach 1:
The patent segments the broad concept class into multiple hierarchical sub-classes (e.g., location divided into country, state, city levels). The system evaluates different segmentation granularities and selects the optimal hierarchy that balances structural simplicity with modeling accuracy for speech recognition tasks.
2Measurement precision
If a language model uses specific narrow classes to represent a concept, then the modeling accuracy improves, but the model complexity increases
Solution Approach 1:
The patent introduces dynamic class selection where the system adapts the granularity of concept representation based on the specific speech recognition task. Different task contexts trigger different levels of hierarchical detail, allowing the model to optimize between accuracy and complexity dynamically rather than using a fixed structure.
3Measurement precision
If the language model uses an intermediate class in the hierarchy, then the accuracy is enhanced, but determining the optimal class requires evaluating multiple models
Solution Approach 1:
The patent performs preliminary evaluation of different class granularities during the model training phase using validation datasets. By pre-assessing which hierarchical levels yield best performance for different concepts and tasks, the system establishes optimal class selections in advance, avoiding the need to evaluate multiple models during deployment.
Solution Approach 2:
The system implements feedback mechanisms using performance metrics (word error rate, perplexity) to evaluate different class granularities. This feedback loop allows the system to identify optimal intermediate classes empirically and adjust the hierarchical representation to maximize accuracy while maintaining reasonable computational efficiency.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating language models. In some implementations, data is accessed that indicates a set of classes corresponding to a concept. A first language model is generated in which a first class represents the concept. A second language model is generated in which second classes represent the concept. Output of the first language model and the second language model is obtained, and the outputs are evaluated. A class from the set of classes is selected based on evaluating the output of the first language model and the output of the second language model. In some implementations, the first class and the second class are selected from a parse tree or other data that indicates relationships among the classes in the set of classes.