Sequence-to-Sequence CNN with Attention for Parallel Processing
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Solution Overview
Problem
Recurrent neural networks (RNNs) face limitations in sequence-to-sequence learning due to their inability to be easily parallelized, leading to slow processing and difficulty in accurately representing long-range dependencies and hierarchical structures in sequences.
Innovation Solution
The use of convolutional neural networks (CNNs) with multiple blocks, residual connections, and attention mechanisms allows for parallelization and improved accuracy in sequence-to-sequence mapping, capturing long-range dependencies and syntax structures more effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If recurrent neural networks are used for sequence-to-sequence learning, then the network can maintain state of previous events, but the network cannot be easily parallelized and runs slowly on hardware
Solution Approach 1:
The patent segments the sequence processing into independent parallel operations by replacing the sequential RNN architecture with a CNN that can process multiple positions simultaneously through convolutional filters, while still capturing temporal dependencies through the convolutional operation
Solution Approach 2:
The patent substitutes the mechanical sequential processing mechanism of RNNs with a parallel computational mechanism using CNNs and attention masks, where the attention mask provides the state maintenance function without requiring sequential computation
2Ease of operation
If recurrent neural networks operate on sequences in a linear manner, then the processing follows a simple left-to-right flow, but the network has difficulty accurately representing syntax structure and long-range dependencies
Solution Approach 1:
The patent adds a dimensional perspective by using 2D convolutional filters that can capture both local sequential patterns and longer-range dependencies simultaneously, transforming the purely linear processing into a multi-dimensional feature extraction process that better represents syntactic structures
Solution Approach 2:
The patent introduces attention masks as an intermediary mechanism that enables the CNN to selectively focus on relevant positions in the sequence, allowing the model to capture long-range dependencies and hierarchical structures without abandoning the parallel processing advantage
Data Source
AI summary
Exemplary embodiments relate to improvements to neural networks for translation and other sequence-to-sequence tasks. A convolutional neural network may include multiple blocks, each having a convolution layer and gated linear units; gating may determine what information passes through to the next block level. Residual connections, which add the input of a block back to its output, may be applied around each block. Further, an attention may be applied to determine which word is most relevant to translate next. By applying repeated passes of the attention to multiple layers of the decoder, the decoder is able to work on the entire structure of a sentence at once (with no temporal dependency). In addition to better accuracy, this configuration is better at capturing long-range dependencies, better models the hierarchical syntax structure of a sentence, and is highly parallelizable and thus faster to run on hardware.