Language Model Optimization via Iterative Parsing
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Solution Overview
Problem
Existing automatic speech recognition (ASR) systems using discrete words as basic units are limited by short memory spans and inefficiencies in generalizing from limited training data, leading to contextually unaware and inefficient language modeling.
Innovation Solution
An iterative optimization algorithm that expresses training material as alternative representations of higher-level units, such as named entities and carrier phrases, to create a unified language model that combines the strengths of words, classes, and phrases, optimizing the model through iterative parsing and retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If discrete words (n-grams) are used as basic units for language modeling, then the model maintains simplicity and scalability, but the model suffers from short memory spans and inability to efficiently generalize from limited training data
Solution Approach 1:
The patent segments the training corpus into multiple levels of linguistic units: discrete words, multi-word units (phrases), and named entities. This hierarchical segmentation allows the model to capture both fine-grained word-level patterns and coarse-grained phrase-level structures, thereby extending memory span and improving generalization while maintaining computational tractability through the structured organization of segments at different levels.
2Adaptability or versatility
If larger-span units or multi-word units are used in addition to discrete words, then generalization capability improves for domain-constrained applications, but the model complexity increases
Solution Approach 1:
The patent implements a nested structure where multi-word units and named entities are embedded within the broader language model framework. Specifically, phrases and named entities are treated as higher-level tokens that contain and organize smaller linguistic units within them. This nesting allows the model to capture long-range dependencies and domain-specific patterns without requiring a complete redesign of the underlying n-gram architecture, thus managing complexity through hierarchical organization.
Solution Approach 2:
The patent applies local quality by allowing different parts of the language model to have different granularities. Discrete words maintain their traditional role for general vocabulary, while multi-word units and named entities are introduced locally in regions where they provide benefit (e.g., domain-specific terminology, common phrases). This selective application of complexity only where needed improves generalization for domain-constrained applications without uniformly increasing model complexity across the entire vocabulary.
3Adaptability or versatility
If class-based language models are used to mitigate weaknesses of discrete words, then generalization improves, but the methods become inefficient and contextually unaware
Solution Approach 1:
The patent introduces dynamics by making the linguistic units adaptable rather than fixed. The model can dynamically select between discrete words, multi-word units, and named entities based on the context and the specific linguistic pattern being modeled. This dynamic selection allows the system to efficiently generalize across different contexts by choosing the appropriate level of abstraction, rather than being constrained to a single static representation scheme for all cases.
Data Source
AI summary
Systems and methods are provided for optimizing language models for in-domain applications through an iterative, joint-modeling approach that expresses training material as alternative representations of higher-level tokens, such as named entities and carrier phrases. From a first language model, an in-domain training corpus may be represented as a set of alternative parses of tokens. Statistical information determined from these parsed representations may be used to produce a second (or updated) language model, which is further optimized for the domain. The second language model may be used to determine another alternative parsed representation of the corpus for a next iteration, and the statistical information determined from this representation may be used to produce a third (or further updated) language model. Through each iteration, a language model may be determined that is further optimized for the domain.