Class-Based Language Model Clustering for Precision
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Solution Overview
Problem
Existing language models require extensive diversity and volume of training data to accurately predict sequences of terms, especially for classes like dates, percentages, and locations, which can lead to inefficiencies and reduced precision in speech recognition and machine translation tasks.
Innovation Solution
The development of class-based language models that break down super-classes into clustered sub-classes, allowing for more precise context modeling by grouping terms with similar usage patterns, thereby reducing the need for extensive training data and improving recognition accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a conventional class-based language model uses a single super-class for terms like dates, then the model structure is simple, but the precision in recognizing specific date patterns is reduced
Solution Approach 1:
The patent segments a super-class (e.g., DATE) into multiple sub-classes (e.g., specific date patterns like '21st of April', '13th of March') based on contextual similarity. Each sub-class captures specific usage patterns, enabling the language model to distinguish subtle differences in date expressions while maintaining an organized hierarchical structure that balances precision and complexity.
2Reliability
If a non-class based language model uses diverse text samples with broad spectrum of terms, then the training data diversity is high, but the amount of training data required is substantially greater
Solution Approach 1:
The patent creates a class-based framework where a single training sample with a specific date instance (e.g., '21st of April') can be generalized to represent the entire DATE class. The model learns contextual patterns from specific instances and applies them universally to recognize various date expressions, reducing the need for extensive diverse training data while maintaining reliability.
3Ease of manufacture
If a class-based language model groups all date terms under a single class, then the model is easier to train, but the ability to detect subtle differences among date terms is reduced
Solution Approach 1:
The patent applies local quality by creating sub-classes with specialized characteristics for different date patterns. Each sub-class is tailored to capture specific contextual nuances (e.g., ordinal dates vs. cardinal dates, different month formats), allowing the model to maintain ease of training through hierarchical organization while achieving high precision in detecting subtle differences through context-specific sub-class recognition.
Data Source
AI summary
This document describes, among other things, a computer-implemented method. The method can include obtaining a plurality of text samples that each include one or more terms belonging to a first class of terms. The plurality of text samples can be classified into a plurality of groups of text samples. Each group of text samples can correspond to a different sub-class of terms. For each of the groups of text samples, a sub-class context model can be generated based on the text samples in the respective group of text samples. Particular ones of the sub-class context models that are determined to be similar can be merged to generate a hierarchical set of context models. Further, the method can include selecting particular ones of the context models and generating a class-based language model based on the selected context models.