Graph-to-Text Network Biaffine Attention Structural Preservation
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Solution Overview
Problem
Existing graph-to-text generation models often drop core structural information from input graphs when generating text, leading to a loss of meaning and accuracy in the output.
Innovation Solution
The proposed method involves training a graph-to-text generation network using two types of auto-encoding losses that focus on different aspects of input graphs. These losses are based on a biaffine attention score for reconstructing triple relations and a linearized sequence for predicting the whole graph, which helps preserve structural information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing graph-to-text generation models are used to generate text from input graphs, then text generation speed is improved, but structural information from input graphs is lost
Solution Approach 1:
The patent segments the graph structure into multiple components: node embeddings, edge embeddings, and attention mechanisms. Each component processes specific aspects of the graph structure independently, allowing the model to capture different structural features (local connectivity, global topology, node relationships) separately and combine them to preserve comprehensive structural information during text generation.
Solution Approach 2:
The patent implements nested encoding layers where node embeddings are embedded within edge embeddings, which are in turn embedded within the final graph representation. This hierarchical nesting allows multiple levels of structural information to be preserved simultaneously, with each layer contributing different aspects of the graph structure to the final text generation.
2Speed
If graph structure is simplified for faster processing, then processing speed is improved, but meaning preservation is worsened
Solution Approach 1:
The patent applies local quality by using different embedding strategies for different parts of the graph structure. Node embeddings capture local node properties, while edge embeddings capture local relationship patterns. The attention mechanism then selectively weights these local features based on their importance to the overall meaning, allowing fast processing of local features while preserving global meaning through selective aggregation.
Solution Approach 2:
The patent dynamically changes parameters during processing by using learnable embedding dimensions and attention weights. The model adjusts the importance of different structural parameters (node features, edge features, positional information) based on the specific graph being processed, enabling fast processing of simple graphs while maintaining high fidelity for complex graphs where meaning preservation is critical.
Data Source
AI summary
A method, computer program, and computer system for training a graph-to-text generation network is provided. Encoded graph information corresponding to a target sentence is received, and the encoded graph information is decoded based on a biaffine attention score. One or more loss values are determined based on the decoded information, whereby the text-to-graph generation network is trained by minimizing the one or more loss values. A first loss value is generated by reconstructing one or more triple relations based on the biaffine attention score, and a second loss value predicts the graph as a linearized sequence.