Dilated Convolutional Neural Network for Text Sequence Processing
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Solution Overview
Problem
Current machine learning models, particularly deep neural networks, face challenges in efficiently processing text sequences for tasks like translation and summarization, often requiring extensive computation and struggling with maintaining representation resolution, which affects translation quality and efficiency.
Innovation Solution
A neural machine translation system utilizing a dilated convolutional neural network architecture with masked layers and sub-batch normalization, which processes input sequences to generate target embeddings while preserving resolution and reducing computational footprint, enabling efficient and high-quality translations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional RNN-based architectures are used for processing text sequences, then the model can capture sequential dependencies, but the training and inference times are excessive and computational efficiency is poor
Solution Approach 1:
The patent replaces the sequential processing mechanism of RNNs with a parallel processing mechanism using dilated convolutional neural networks. The convolutional layers process multiple time steps simultaneously through parallel computation, eliminating the sequential bottleneck of RNNs while maintaining the ability to capture temporal dependencies through dilated convolutions with increasing receptive fields.
Solution Approach 2:
The patent introduces a spatial dimension to the temporal processing problem by using dilated convolutions that expand the receptive field across time steps. Instead of processing sequences step-by-step in one dimension, the dilated convolutional layers create a multi-scale temporal representation by applying filters with different dilation rates, effectively adding a dimensional transformation to the processing architecture.
2Adaptability or versatility
If deep neural networks are used to process text sequences, then the model can learn complex patterns, but the representation resolution deteriorates affecting translation quality
Solution Approach 1:
The patent segments the deep processing into multiple dilated convolutional layers, each with a specific dilation rate. This segmentation allows the model to process temporal patterns at different scales simultaneously, with each layer preserving representation resolution for its specific temporal receptive field while collectively capturing complex multi-scale patterns across all layers.
Solution Approach 2:
The patent applies local quality by assigning different dilation rates to different layers, allowing each layer to specialize in capturing patterns at specific temporal scales. This enables the model to maintain high representation resolution for local patterns while also capturing global dependencies, with each layer optimized for its specific temporal resolution requirements.
3Productivity
If convolutional networks are used instead of RNNs, then computational efficiency improves, but the ability to capture long-range dependencies may be reduced
Solution Approach 1:
The patent introduces dynamic receptive fields through progressively increasing dilation rates across layers. The dilation rate doubles at each subsequent layer (1, 2, 4, 8, 16, 32), creating a dynamic expansion of the temporal receptive field that allows the network to capture long-range dependencies while maintaining computational efficiency through parallel convolutional operations rather than sequential RNN processing.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for language modeling. In one aspect, a system comprises: a masked convolutional decoder neural network that comprises a plurality of masked convolutional neural network layers and is configured to generate a respective probability distribution over a set of possible target embeddings at each of a plurality of time steps; and a modeling engine that is configured to use the respective probability distribution generated by the decoder neural network at each of the plurality of time steps to estimate a probability that a string represented by the target embeddings corresponding to the plurality of time steps belongs to the natural language.