Maximum Entropy Language Model Backoff Features
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Solution Overview
Problem
Maximum entropy models face challenges in effectively combining information from various model components, particularly in smoothing and regularization, where backoff mechanisms are complex and require extensive parameter estimation, and existing methods do not adequately utilize features like backoff weights to improve language modeling performance.
Innovation Solution
Incorporating backoff features and suffix/prefix backoff features into maximum entropy language models, which are trained using stochastic gradient descent and the MapReduce framework, to enhance the model's ability to handle unseen n-grams and improve language modeling accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If backoff features are incorporated into maximum entropy language models, then the model's ability to handle unseen n-grams improves, but the device complexity and parameter estimation requirements increase
Solution Approach 1:
The patent segments the language model into distinct feature types (n-gram features, backoff features, suffix backoff features, prefix backoff features) that can be independently computed and combined. This segmentation allows the model to handle unseen n-grams by activating specific backoff feature subsets without requiring complete model restructuring.
Solution Approach 2:
The backoff features are designed to serve multiple functions: they provide fallback mechanisms for unseen n-grams, capture suffix and prefix contextual information, and work across different n-gram orders. This multi-functionality reduces the need for separate specialized components for each type of contextual information.
2Manufacturing precision
If backoff features and suffix/prefix backoff features are incorporated into maximum entropy language models, then language modeling accuracy improves, but the training complexity and computational resources required increase
Solution Approach 1:
The patent pre-computes and stores backoff feature templates during model construction, including suffix backoff features that capture historical context and prefix backoff features that capture future context. This preliminary action reduces the computational burden during training by avoiding real-time computation of all possible backoff features.
Solution Approach 2:
The model dynamically activates different combinations of backoff features based on the specific n-gram being evaluated. For unseen n-grams, the model automatically activates backoff features while keeping n-gram features inactive, allowing adaptive complexity management during training and inference.
3Device complexity
If existing maximum entropy models are used without backoff features, then the model structure remains simple, but the performance on unseen contexts deteriorates
Solution Approach 1:
The backoff features act as intermediaries between the simple n-gram features and the unseen contexts. When n-gram features fail to match observed patterns, backoff features provide alternative matching mechanisms that bridge the gap to unseen contexts without requiring complete model restructuring.
Solution Approach 2:
The patent incorporates backoff features as a safety mechanism that is prepared in advance during model construction. These features provide a cushion or fallback option that activates when primary n-gram features fail, preventing performance deterioration on unseen contexts before it occurs.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, relating to enhanced maximum entropy models. In some implementations, data indicating a candidate transcription for an utterance and a particular context for the utterance are received. A maximum entropy language model is obtained. Feature values are determined for n-gram features and backoff features of the maximum entropy language model. The feature values are input to the maximum entropy language model, and an output is received from the maximum entropy language model. A transcription for the utterance is selected from among a plurality of candidate transcriptions based on the output from the maximum entropy language model. The selected transcription is provided to a client device.