Recurrent Neural Network Online Sequence Generation
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Solution Overview
Problem
Conventional attention-based sequence-to-sequence models require processing the entire input sequence before generating outputs, limiting their ability to perform online tasks like voice recognition and translation efficiently and accurately.
Innovation Solution
A recurrent neural network system that includes a recurrent neural network and an attention subsystem, capable of generating output sequences online by processing a current input and previous outputs to determine when and what to emit, using progress scores and output scores to decide on output emission and selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional attention-based sequence-to-sequence models process the entire input sequence before generating outputs, then the model can utilize complete input information for accurate output generation, but the system cannot perform online tasks efficiently and must wait for full sequence processing
Solution Approach 1:
The system performs preliminary processing by maintaining a hidden state that accumulates information from input tokens as they arrive, rather than waiting for the complete sequence. The attention mechanism computes attention weights and generates outputs incrementally based on currently available information, allowing online processing while preserving the ability to incorporate future tokens into the hidden state for improved accuracy.
2Power
If the system generates outputs before processing the entire input sequence, then online processing becomes possible with reduced computational requirements, but the model has access to less complete input information
Solution Approach 1:
The system uses feedback by feeding the generated output back into the recurrent neural network along with subsequent input tokens. This allows the model to continuously refine its understanding as more input becomes available, incorporating future information into the hidden state which then influences subsequent outputs, effectively allowing the model to benefit from complete sequence information even when generating outputs incrementally.
Solution Approach 2:
The attention mechanism dynamically adjusts attention weights based on the current hidden state and available input tokens. As the sequence progresses and more tokens become available, the attention distribution evolves to reflect the updated context, allowing the system to adaptively utilize available information at each timestep rather than being constrained by a fixed processing paradigm.
3Measurement precision
If conventional models wait for complete input sequence processing, then they can ensure accurate output generation, but the system complexity and processing time increase
Solution Approach 1:
The system segments the processing into incremental timesteps where outputs are generated based on progressively available input information. Rather than requiring complete sequence processing as a monolithic operation, the attention mechanism and recurrent neural network divide the task into manageable segments that can be executed online, reducing architectural complexity while maintaining accuracy through continuous refinement as more tokens arrive.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a target sequence from a source sequence. In one aspect, the system includes a recurrent neural network configured to, at each time step, receive am input for the time step and process the input to generate a progress score and a set of output scores; and a subsystem configured to, at each time step, generate the recurrent neural network input and provide the input to the recurrent neural network; determine, from the progress score, whether or not to emit a new output at the time step; and, in response to determining to emit a new output, select an output using the output scores and emit the selected output as the output at a next position in the output order.