Sequence Modeling Using Imputation for Neural Network Alignment
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Solution Overview
Problem
Existing neural network systems for generating sequential data are inefficient, particularly in real-time applications, as they often rely on auto-regressive models that increase runtime with input sequence length and make conditional independence assumptions that lead to poor performance.
Innovation Solution
A system that processes input sequences using a neural network to generate a latent alignment, allowing for the generation of output sequences in a fixed number of time steps by segmenting the input sequence into blocks and generating new elements in the latent alignment at each time step, thus avoiding conditional independence assumptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If auto-regressive neural networks are used to generate sequential data, then the model can capture temporal dependencies, but the runtime increases with input sequence length
Solution Approach 1:
The patent segments the input sequence into multiple blocks and processes each block independently through the neural network. This segmentation allows parallel processing of different blocks, reducing the overall runtime while maintaining the ability to capture temporal dependencies within each block. The segmentation principle directly addresses the contradiction by dividing the sequential processing into parallelizable units.
Solution Approach 2:
The patent introduces a block dimension by dividing the sequence into blocks, transforming the problem from purely sequential processing to a multi-dimensional structure. This allows the model to process multiple blocks simultaneously (adding a parallel dimension) while still capturing temporal relationships within each block, thereby reducing runtime without sacrificing temporal dependency capture.
2Productivity
If non-auto-regressive neural networks are used to generate output sequences, then the generation speed increases, but the accuracy decreases due to conditional independence assumptions
Solution Approach 1:
By segmenting the sequence into blocks and processing each block independently, the patent achieves faster generation speeds through parallel processing while maintaining accuracy by ensuring each block is processed with full contextual information from the input sequence. The segmentation allows non-auto-regressive processing within blocks while preserving global dependencies.
Solution Approach 2:
The patent performs preliminary encoding of the entire input sequence into contextual representations before generating output blocks. This preliminary action allows each output block to be generated independently (non-auto-regressively) while still having access to comprehensive contextual information, thereby maintaining accuracy without sacrificing generation speed.
3Reliability
If the neural network processes the entire input sequence at once, then the model captures global dependencies, but the computational complexity increases
Solution Approach 1:
The patent segments the input sequence into multiple blocks that can be processed in parallel, reducing computational complexity compared to processing the entire sequence at once. Each block is processed independently with access to global contextual representations, maintaining global dependency capture while lowering the computational burden through parallelization.
Solution Approach 2:
The patent introduces a block structure dimension, transforming the computational problem from a single large processing unit to multiple smaller parallel units. This dimensional change reduces the computational complexity of each individual processing unit while maintaining global dependencies through shared contextual representations across all blocks.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for sequence modeling. One of the methods includes receiving an input sequence having a plurality of input positions; determining a plurality of blocks of consecutive input positions; processing the input sequence using a neural network to generate a latent alignment, comprising, at each of a plurality of input time steps: receiving a partial latent alignment from a previous input time step; selecting an input position in each block, wherein the token at the selected input position of the partial latent alignment in each block is a mask token; and processing the partial latent alignment and the input sequence using the neural network to generate a new latent alignment, wherein the new latent alignment comprises, at the selected input position in each block, an output token or a blank token; and generating, using the latent alignment, an output sequence.