Recurrent Neural Network Token Skipping for Long Text Processing
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Solution Overview
Problem
Conventional text processing systems using recurrent neural networks are inefficient in processing long text sequences, as they require processing every token, leading to increased computational resources and reduced prediction accuracy due to irrelevant data processing.
Innovation Solution
A system that processes text sequences by skipping designated tokens, using a recurrent neural network to update internal states and a jump prediction neural network to determine which tokens to skip, thereby focusing on informative portions and reducing computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If every token in a long text sequence is processed by a recurrent neural network, then complete information is captured, but computational resource consumption increases and processing speed decreases
Solution Approach 1:
The text sequence is segmented into multiple segments, and the RNN processes only selected segments rather than every token. The system divides the input sequence and applies selective processing to different portions, achieving both efficiency and accuracy.
Solution Approach 2:
The system extracts and processes only the most informative tokens from the text sequence, skipping less important ones. This extraction of key information allows the model to maintain prediction accuracy while reducing computational load significantly.
2Measurement precision
If every token in a long text sequence is processed by a recurrent neural network, then complete information is captured, but resource consumption increases
Solution Approach 1:
The system applies partial action by processing only a subset of tokens rather than the complete sequence. The RNN performs computations on selected informative portions, reducing energy and resource consumption while maintaining sufficient accuracy for the task.
3Adaptability or versatility
If irrelevant tokens are processed, then comprehensive analysis is performed, but prediction accuracy decreases due to noise
Solution Approach 1:
The system extracts and removes irrelevant or noisy tokens from the text sequence before processing. By filtering out harmful factors and keeping only informative content, the model achieves better prediction accuracy while maintaining adaptability to different text types.
Solution Approach 2:
The system converts the potential harm of processing irrelevant tokens into benefit by using attention mechanisms to identify and emphasize informative tokens while suppressing noise. The harmful effect of noise is transformed into a learning opportunity for the attention model to distinguish relevant from irrelevant content.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing sequential data. In one aspect, a computer-implemented method includes receiving a request to generate a system output for an input data sequence, the input data sequence including a plurality of tokens. One or more tokens may be designated as tokens to be skipped. When a token has not been designated as a token to be skipped, the token is processed using a recurrent neural network to update a current internal state of the recurrent neural network. The system output is generated from the final internal state of the recurrent neural network.