End-To-End Graph Convolution Network for Long-Range NLP Dependencies
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Solution Overview
Problem
Existing natural language processing systems struggle with capturing long-range dependencies and multi-hop inferences in text, particularly in tasks like sequence tagging and classification, often requiring manual graph transformation and lacking efficient multi-task learning.
Innovation Solution
An end-to-end differentiable graph convolutional neural network that transforms input sequences into latent graphs for unsupervised learning, employing a contextualization layer, dimension-preserving convolution, and graph-based message-passing to capture dependencies between words.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If hand-crafted graph transformation methods are used, then graph structure can be constructed, but the process requires diverse third party systems and manual intervention
Solution Approach 1:
The patent replaces manual, hand-crafted graph transformation processes with an automated neural network-based approach. The neural network directly transforms input sequences into graph structures through learned representations, eliminating the need for complex third-party systems and manual graph construction steps.
Solution Approach 2:
The system performs self-service by automatically learning and optimizing its own graph transformation capabilities through training on labeled data. The neural network learns to construct meaningful graph structures from raw text sequences without requiring external manual intervention or complex pre-processing pipelines.
2Productivity
If recurrent neural networks are used for sequence processing, then sequential data can be processed, but long range dependencies between sentences and paragraphs cannot be effectively captured
Solution Approach 1:
The patent transitions from linear sequential processing to a multi-dimensional graph representation. By transforming sequences into graphs with nodes and edges representing semantic relationships, the system captures long-range dependencies that span across sentences and paragraphs, adding a relational dimension to the processing approach.
Solution Approach 2:
The graph convolutional neural network serves multiple functions simultaneously: it processes sequential data, captures long-range dependencies, performs graph structure learning, and enables multi-task learning for various NLP tasks. This multi-functional approach resolves the limitation of recurrent networks by integrating multiple capabilities into a single unified model.
3Reliability
If graph convolutional neural networks are used, then global inference can be performed, but the input text must be transformed into graph structures requiring manual intervention
Solution Approach 1:
The patent replaces manual graph transformation operations with an automated neural network-based graph construction process. The neural network learns to directly transform input text sequences into graph structures through trained representations, eliminating manual intervention while maintaining the ability to perform accurate global inference.
4Adaptability or versatility
If separate graph encoder and task-specific models are used, then multi-task learning can be achieved, but the system requires separate training for each task
Solution Approach 1:
The graph convolutional neural network is designed as a universal model that can perform multiple NLP tasks through a single unified architecture. The same graph encoder and message-passing mechanism can be applied to different tasks such as named entity recognition, relation extraction, and sequence classification, eliminating the need for separate task-specific models and reducing overall training time.
Solution Approach 2:
The patent merges the graph encoder and task-specific processing into a single integrated model. By combining the graph construction, message-passing, and task-specific output layers into one unified neural network, the system achieves multi-task learning capability while reducing the time required for training and deployment compared to separate models.
Data Source
AI summary
A natural language sentence includes a sequence of tokens. A system for entering information provided in the natural language sentence to a computing device includes a processor and memory coupled to the processor, the memory including instructions executable by the processor implementing: a contextualization layer configured to generate a contextualized representation of the sequence of tokens; a dimension-preserving convolutional neural network configured to generate an output matrix from the contextualized representation; and a graph convolutional neural network configured to: use the matrix to form a set of adjacency matrices; and generate a label for each token in the sequence of tokens based on hidden states for that token in a last layer of the graph convolutional neural network.