Backoff Strategy for Missing Features in Language Models
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Solution Overview
Problem
Log-linear models and other language models often inaccurately estimate the likelihood of unseen outcomes and contexts due to missing features, leading to overestimation of likelihoods and incorrect indications, particularly in speech recognition and natural language processing tasks.
Innovation Solution
Implementing a backoff strategy where a minimum weight is assigned to missing features based on the lowest weight of observed features in the same context, ensuring that unobserved outcomes are assigned a lower likelihood than observed ones, thereby correcting the model's predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a language model uses features from training data to estimate likelihoods, then observed outcomes are accurately scored, but unobserved outcomes receive incorrect high likelihood estimates due to missing features
Solution Approach 1:
The patent pre-computes minimum scores for all contexts during the training phase, storing them for later use. This preliminary action ensures that when unobserved outcomes are encountered during inference, the model can immediately apply appropriate minimum scoring without ad-hoc calculations, thereby maintaining consistency and reliability in likelihood estimates for both observed and unobserved outcomes.
Solution Approach 2:
The patent modifies the scoring mechanism by introducing context-dependent minimum scores as a new parameter. Instead of using a fixed default score for unobserved outcomes, the model dynamically adjusts the minimum score based on the specific context, changing the parameter space to better reflect the actual likelihood distribution and improve measurement precision for unobserved outcomes.
2Ease of operation
If the model assigns default scores to missing features, then computation is simplified, but unobserved words are unintentionally prioritized over observed words
Solution Approach 1:
The patent applies different scoring strategies to different contexts based on their observed characteristics. For each context, the model computes and stores a specific minimum score that reflects the local properties of that context. This local quality approach ensures that unobserved outcomes in different contexts receive appropriately differentiated minimum scores, preventing uniform default scoring from causing transcription errors while maintaining computational efficiency through pre-computation.
Data Source
AI summary
Systems and methods for addressing missing features in models are provided. In some implementations, a model configured to indicate likelihoods of different outcomes is accessed. The model includes a respective score for each of a plurality of features, and each feature corresponds to an outcome in an associated context. It is determined that the model does not include a score for a feature corresponding to a potential outcome in a particular context. A score is determined for the potential outcome in the particular context based on the scores for one or more features in the model that correspond to different outcomes in the particular context. The model and the score are used to determine a likelihood of occurrence of the potential outcome.