Structure-Preserving Attention for Stable Seq2Seq Alignment
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Solution Overview
Problem
Existing seq2seq neural models struggle with alignment matrix modification during inference, leading to instability and suboptimal output sequence generation, particularly in tasks like text-to-speech where preserving structural alignment is crucial.
Innovation Solution
A structure-preserving secondary attention mechanism is introduced, which generates secondary attention vectors that adhere to a desired alignment structure, using a trained soft-selection ANN to evaluate and combine attention vector candidates, ensuring accurate output sequence prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a primary attention mechanism is used to generate attention vectors in seq2seq models, then the model can process input sequences and generate output sequences, but the alignment matrix structure becomes unstable during inference time
Solution Approach 1:
The patent introduces a secondary attention mechanism as an intermediary component that works alongside the primary attention mechanism. This secondary mechanism specifically enforces the desired monotonic structure on the alignment matrix, acting as a mediator that corrects structural deviations without completely replacing the primary attention computation. The soft-selection ANN combines outputs from both mechanisms, weighting their contributions to achieve stable alignment.
Solution Approach 2:
The attention mechanism is segmented into two distinct functional components: a primary attention mechanism that handles general attention computation and a secondary attention mechanism that specifically enforces structural constraints. This segmentation allows each component to specialize in its function, with the primary mechanism maintaining flexibility and the secondary mechanism ensuring structural stability.
2Manufacturing precision
If alignment matrix structure is not preserved during inference, then the model has flexibility in generating output sequences, but the convergence during learning deteriorates
Solution Approach 1:
The secondary attention mechanism provides feedback to the primary attention mechanism by evaluating whether the generated alignment matrix satisfies the desired monotonic structure. The soft-selection ANN uses this structural information as feedback to adjust the weighting between primary and secondary attention outputs, ensuring that learning convergence is maintained while preserving output flexibility.
3Reliability
If a secondary attention mechanism is added to preserve alignment structure, then alignment stability and convergence improve, but the device complexity increases
Solution Approach 1:
The patent changes the parameter space by introducing a continuous weighting parameter in the soft-selection ANN that balances the contributions of primary and secondary attention mechanisms. This parameter can be adjusted during training and inference to optimize the trade-off between structural enforcement and computational efficiency, allowing the system to adapt to different operational requirements.
Data Source
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AI summary
In a trained attentive decoder of a trained Sequence-to-Sequence (seq2seq) Artificial Neural Network (ANN): obtaining an encoded input vector sequence; generating, using a trained primary attention mechanism of the trained attentive decoder, a primary attention vectors sequence; for each primary attention vector of the primary attention vectors sequence: (a) generating a set of attention vector candidates corresponding to the respective primary attention vector, (b) evaluating, for each attention vector candidate of the set of attention vector candidates, a structure fit measure that quantifies a similarity of the respective attention vector candidate to a desired attention vector structure, (c) generating, using a trained soft-selection ANN, a secondary attention vector based on said evaluation and on state variables of the trained attentive decoder; and generating, using the trained attentive decoder, an output sequence based on the encoded input vector sequence and the secondary attention vectors.