Pointer Sentinel Mixture Architecture for Neural Network Sequence Models
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Solution Overview
Problem
Neural network sequence models face challenges in predicting rare and out-of-vocabulary tokens due to limited hidden state capacity and the vanishing gradient problem, leading to inefficiencies in language modeling, especially when context is not adequately utilized.
Innovation Solution
The pointer sentinel mixture architecture combines a pointer network with a softmax classifier, using a sentinel gate vector to decide when to use the vocabulary, allowing the pointer network to select tokens from context and improving prediction accuracy for rare and unseen words by integrating attention mechanisms and reducing reliance on hidden state capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard softmax LSTM models are used to achieve best language modeling performance, then prediction accuracy is improved, but the number of parameters increases significantly
Solution Approach 1:
The model segments the prediction task into two distinct components: a pointer network that selects from recent context tokens and a softmax classifier that predicts from a predefined vocabulary. This segmentation allows each component to specialize, with the pointer network handling rare and out-of-vocabulary tokens while the softmax classifier handles common tokens, achieving state-of-the-art language modeling performance with fewer parameters than a standard softmax LSTM.
2Measurement precision
If hidden state capacity is increased to predict rare tokens, then prediction accuracy is improved, but computational overhead increases
Solution Approach 1:
The invention extracts the token selection function from the hidden state and implements it through a separate pointer network with attention mechanisms. Instead of relying on increasing hidden state capacity to capture rare tokens, the pointer network explicitly attends to and selects from recent context tokens, making the computational process more efficient and scalable.
3Measurement precision
If pointer network is used to select tokens from context, then prediction accuracy for rare tokens is improved, but device complexity increases
Solution Approach 1:
The invention merges the pointer network and softmax classifier into a unified mixture architecture where both components work together to predict the next token. The pointer network handles rare and out-of-vocabulary tokens by selecting from context, while the softmax classifier handles common tokens from vocabulary, achieving state-of-the-art language modeling performance with fewer parameters than a standard softmax LSTM.
Data Source
AI summary
The technology disclosed provides a so-called “pointer sentinel mixture architecture” for neural network sequence models that has the ability to either reproduce a token from a recent context or produce a token from a predefined vocabulary. In one implementation, a pointer sentinel-LSTM architecture achieves state of the art language modeling performance of 70.9 perplexity on the Penn Treebank dataset, while using far fewer parameters than a standard softmax LSTM.