Deep Recurrent Neural Network with Layer-Wise Attention for Punctuation Restoration
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Solution Overview
Problem
Conventional punctuation restoration systems are inefficient and inaccurate due to their reliance on computationally expensive models and simple neural network structures that fail to thoroughly analyze sequences of words, leading to suboptimal punctuation prediction.
Innovation Solution
A deep recurrent neural network architecture with multiple bi-directional recurrent layers and neural attention mechanisms, including scaled dot-product and multi-head attentions, is employed to generate probabilities for punctuation marks, enabling more accurate and efficient punctuation restoration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional punctuation restoration systems use simple neural network structures, then the device complexity is reduced, but the measurement precision of punctuation prediction deteriorates
Solution Approach 1:
The patent segments the neural network into multiple specialized bi-directional recurrent layers, where each layer processes the word sequence from different directional perspectives (forward and backward). This segmentation allows the system to capture contextual information more effectively without requiring a single overly complex network structure, thereby improving punctuation prediction accuracy while maintaining manageable complexity.
Solution Approach 2:
The patent introduces a new dimension by applying attention mechanisms that operate across the sequence of hidden states generated by the recurrent layers. Instead of simply stacking layers vertically, the attention mechanism adds a horizontal dimension of interaction, allowing the model to weigh and combine information from different positions in the sequence, thus improving prediction accuracy without proportionally increasing structural complexity.
2Measurement precision
If conventional punctuation restoration systems use computationally expensive models, then the measurement precision of punctuation prediction is improved, but the productivity of the system deteriorates
Solution Approach 1:
The patent implements partial action by applying attention mechanisms selectively to the hidden states of bi-directional recurrent layers rather than processing all possible combinations of network outputs. The attention mechanism focuses computational resources on the most relevant contextual information for each prediction point, achieving high accuracy without the exponential computational cost of exhaustive analysis.
Solution Approach 2:
The patent changes the parameter of computational complexity by using bi-directional recurrent layers with attention mechanisms, which have linear or near-linear time complexity relative to sequence length, compared to the quadratic or higher complexity of conventional punctuation restoration models. This parameter change maintains prediction accuracy while dramatically improving computational efficiency and productivity.
3Device complexity
If conventional punctuation restoration systems use simple analysis methods, then the device complexity is reduced, but the reliability of punctuation restoration deteriorates
Solution Approach 1:
The patent applies preliminary action by first processing the word sequence through multiple bi-directional recurrent layers to generate comprehensive hidden states that capture contextual information from both forward and backward directions. These pre-computed hidden states serve as the foundation for the attention mechanism, which then performs the final punctuation prediction. This preliminary processing ensures reliable restoration accuracy while keeping the overall analysis method structured and manageable.
Solution Approach 2:
The patent introduces hidden states as an intermediary between the input word sequence and the final punctuation prediction. These hidden states act as a mediator that captures and transforms contextual information in a structured manner, allowing the system to achieve high reliability without requiring direct complex analysis of all possible punctuation contexts. The intermediary hidden states simplify the analysis while preserving accuracy.
Data Source
AI summary
The present disclosure relates to utilizing a deep recurrent neural network for accurately performing punctuation restoration. For example, the disclosed systems can provide a sequence of words to a punctuation restoration neural network having multiple bi-directional recurrent layers and one or more neural attention mechanisms. In one or more embodiments, the punctuation restoration neural network incorporates layer-wise attentions and/or multi-head attention. The disclosed systems can utilize the punctuation restoration neural network to generate probabilities for each word, indicating the likelihood that each possible punctuation mark is associated with that word. Based on these probabilities, the disclosed systems can generate a punctuated transcript that includes punctuation before the appropriate words.