Attention Matrix Rank Reduction for Neural Translation Memory
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Solution Overview
Problem
Neural machine translation models, specifically encoder-decoder paradigms, face challenges in addressing long-distance dependencies and handling variable sentence lengths, leading to diminished translation accuracy due to fixed intermediate state sizes, which restricts the ability to effectively process longer sentences.
Innovation Solution
Implementing a method that limits the attention mechanism's memory usage by decomposing attention matrices with rank reduction, allowing for dynamic adjustment of attention matrices as input sequence length increases, using techniques like singular value decomposition to maintain translation quality within memory constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the intermediate state size is increased to handle longer sentences, then translation accuracy improves, but memory consumption increases
Solution Approach 1:
The patent divides the attention matrix into multiple segments or blocks that can be processed separately. Instead of maintaining a single large attention matrix for the entire sequence, the system processes attention in segmented fashion, reducing peak memory requirements while still capturing long-distance dependencies across the full sequence length.
Solution Approach 2:
The patent transforms the attention computation from a 2D matrix operation that requires storing the entire attention matrix, to a factorized representation using low-rank decomposition. By representing attention as a product of two smaller matrices (U and V), the system reduces memory requirements from O(n²) to O(nk) where k is the rank parameter, effectively adding a dimensional transformation to the problem.
2Reliability
If the attention matrix size is increased to capture long-distance dependencies, then translation quality improves, but the system exceeds memory limits
Solution Approach 1:
The patent implements dynamic rank adjustment where the rank parameter k of the low-rank attention matrix is adaptively adjusted based on the input sequence length and available memory resources. For longer sequences, the system dynamically increases the rank to maintain translation quality, while for shorter sequences it uses lower rank to conserve memory, making the system flexible and adaptive to different operational conditions.
Solution Approach 2:
The patent changes the parameter representation of the attention matrix by introducing a rank parameter k that controls the trade-off between translation quality and memory consumption. By parameterizing the attention mechanism this way, the system can tune the effective capacity of the attention matrix to match available resources while maintaining the ability to capture long-distance dependencies when resources permit.
3Quantity of substance
If a fixed-size intermediate state is used, then memory usage is controlled, but translation accuracy diminishes for longer sentences
Solution Approach 1:
The patent segments the encoding process into multiple stages where different parts of the input sequence are encoded with different levels of attention. Instead of using a single fixed-size intermediate state for the entire sequence, the system creates multiple intermediate representations that are combined, effectively increasing the functional capacity without proportionally increasing peak memory usage.
Solution Approach 2:
The patent performs preliminary encoding of the input sequence into compressed representations before the attention mechanism operates. By pre-processing the input into a condensed form that captures essential features, the system reduces the amount of information that needs to be maintained in the intermediate state, thereby maintaining translation accuracy for long sequences while controlling memory usage through efficient pre-compression.
Data Source
AI summary
A computer-implemented method for translating with limited attention a sequence of input tokens into a sequence of output tokens may include (1) encoding by an encoder recurrent neural network (RNN) N input tokens of a sequence of (N+D) input tokens into an attention matrix limited to N input tokens, (2) iteratively updating a previous version of the attention matrix with the context vector generated from each excess input token yielding a final attention matrix at the last excess input token (D), and (3) decoding an output token using a decoder RNN with inputs from at least the weighted context vector and the just previously-determined output token of the decoder. The weighted context vector may be derived from an attention RNN processing the elements of the final attention matrix for each of the decoder output tokens. Various other methods, systems, and computer-readable media are also disclosed.