Unidirectional RNN Accuracy via Bidirectional Knowledge Transfer
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Solution Overview
Problem
Unidirectional Recurrent Neural Networks (RNNs) typically have lower accuracy compared to bidirectional RNNs due to their sequential output generation, which limits their effectiveness in applications like speech recognition and translation, as they require only preceding inputs, whereas bidirectional RNNs have higher accuracy but longer latency since they process the entire input sequence before outputting results.
Innovation Solution
A method is introduced to improve the accuracy of unidirectional RNNs by training them using knowledge transferred from a bidirectional RNN, where specific outputs from the bidirectional RNN are selected based on similarity and used to adjust the unidirectional RNN's outputs, reducing training loss and improving computational efficiency by focusing on a predetermined range of outputs rather than the entire sequence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a bidirectional RNN is used to improve accuracy, then the accuracy is improved, but the latency increases
Solution Approach 1:
The bidirectional RNN processing is segmented into two separate unidirectional RNNs: a teacher model that processes the entire sequence offline to generate high-accuracy reference outputs, and a student model that processes sequences in real-time. This segmentation allows the system to achieve bidirectional accuracy through the teacher while maintaining unidirectional low latency in the student model.
Solution Approach 2:
The teacher unidirectional RNN performs preliminary processing of the entire input sequence offline before the student model needs to generate real-time predictions. By pre-computing and storing the high-accuracy reference outputs from the teacher model, the system eliminates the need for the student model to wait for future inputs, thus reducing latency while maintaining accuracy through knowledge transfer.
2Measurement precision
If knowledge transfer from bidirectional RNN to unidirectional RNN is implemented, then the accuracy of unidirectional RNN is improved, but the computational cost increases
Solution Approach 1:
Instead of transferring all outputs from the teacher bidirectional RNN to the student unidirectional RNN, the patent selectively transfers only certain outputs based on predefined criteria. This partial action approach reduces the computational burden of knowledge transfer while still providing sufficient training signals to improve the student model's accuracy effectively.
Solution Approach 2:
The patent extracts and transfers only the necessary knowledge components from the teacher bidirectional RNN to the student unidirectional RNN. By selecting specific outputs rather than transferring the entire output sequence, the system reduces computational costs associated with knowledge transfer while maintaining the essential accuracy-improving information.
Data Source
AI summary
Knowledge transfer between recurrent neural networks is performed by obtaining a first output sequence from a bidirectional Recurrent Neural Network (RNN) model for an input sequence, obtaining a second output sequence from a unidirectional RNN model for the input sequence, selecting at least one first output from the first output sequence based on a similarity between the at least one first output and a second output from the second output sequence; and training the unidirectional RNN model to increase the similarity between the at least one first output and the second output.