Graph Neural Network for Multi-Party Dialogue Parsing
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Solution Overview
Problem
Conversational discourse parsing faces challenges in discovering inter-dependencies between non-adjacent utterances due to error propagation in existing neural models like DeepSequential, which rely on historical predictions rather than implicit structural information.
Innovation Solution
An edge-centric structural self-aware graph neural network (SSA-GNN) is employed for discourse parsing, using edge-specific vectors to capture implicit structural information and incorporating auxiliary loss terms for enhanced representation learning, avoiding error propagation and focusing on direct structural interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If DeepSequential model is used for discourse parsing, then neural network capabilities are utilized for automatic feature extraction, but error propagation occurs due to reliance on historical predictions
Solution Approach 1:
The patent extracts and removes the harmful element of historical prediction dependencies from the model. By using a graph neural network that processes all utterances simultaneously rather than sequentially, the model eliminates the propagation of errors from previous predictions while retaining automatic feature extraction capabilities through neural networks.
Solution Approach 2:
Instead of building discourse structure sequentially from previous predictions (DeepSequential approach), the patent inverts the approach by constructing the complete discourse structure simultaneously through graph neural networks, where all utterances are processed in parallel and relationships are determined based on global context rather than incremental historical predictions.
2Ease of manufacture
If pipeline decoding with maximum spanning tree is used, then discourse structure can be inferred systematically, but the process is complex and computationally intensive
Solution Approach 1:
The patent replaces the mechanical pipeline decoding process with a neural network-based approach. Instead of using handcrafted features followed by maximum spanning tree algorithms, the graph neural network automatically learns discourse relationships through neural computations, simplifying the overall process while maintaining systematic structure inference.
Solution Approach 2:
The patent changes the fundamental parameters of the discourse parsing approach by transitioning from sequential pipeline processing to simultaneous graph-based neural network processing. This parameter change allows the model to capture long-range dependencies and non-adjacent relationships more effectively while reducing computational complexity through parallel processing.
3Ease of operation
If handcrafted features are used for discourse parsing, then interpretability is maintained, but the model lacks the capability to capture complex implicit relationships
Solution Approach 1:
The patent creates a composite approach by combining the interpretability of structured graph representations with the powerful feature extraction capabilities of neural networks. The graph neural network maintains the structured, interpretable discourse framework while using neural networks to automatically learn complex features and relationships from the data.
Data Source
AI summary
A method, computer program, and computer system is provided for parsing multi-party dialogue. Dialogue data having one or more elementary discourse units is received. A local representation and a global representation are determined for each of the elementary discourse units based on performing a pairwise comparison on the elementary discourse units. Relationships between the elementary discourse units are identified based on the determined local and global representations. A contextual link is predicted between non-adjacent elementary discourse units based on the identified relationships.