AMR Coreference Resolution Using VGAE Pretraining
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Solution Overview
Problem
Existing AMR coreference resolution methods are data and resource intensive, leading to errors and high costs, and fail to accurately identify coreferences across multiple sentences.
Innovation Solution
A variational graph autoencoder (VGAE) is used for pretraining AMR coreference resolution, leveraging existing supervision signals and automatic parsing to enhance coreference clustering with minimal resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional AMR coreference resolution methods are used, then coreference resolution can be performed, but the methods are data and resource intensive leading to high costs and errors
Solution Approach 1:
The VGAE model performs self-supervised pretraining by learning to reconstruct AMR graphs from their own structures, utilizing the inherent graph topology and node features without requiring external supervision signals. This self-service mechanism allows the model to learn meaningful representations efficiently, reducing dependency on large annotated datasets while improving coreference resolution accuracy
Solution Approach 2:
The approach applies preliminary pretraining using VGAE to learn robust graph representations before the actual coreference resolution task. This preliminary action of pretraining on graph reconstruction enables the model to capture structural patterns and semantic relationships in advance, leading to better performance with reduced resource requirements during the main task execution
2Ease of operation
If string matching is used to detect coreference, then simple detection is possible, but errors occur because concepts with the same surface string may not point to the same entity
Solution Approach 1:
The invention replaces the mechanical string matching approach with a graph neural network-based VGAE model that processes AMR graph structures. This substitution enables the system to consider semantic relationships, node features, and graph topology instead of relying solely on surface string similarity, thereby improving coreference detection accuracy while maintaining operational efficiency through automated graph processing
3Adaptability or versatility
If pipeline systems with textual coreference resolution models are used, then many situations can be resolved, but severe error propagation occurs
Solution Approach 1:
The VGAE model serves as an intermediary that directly processes AMR graphs to generate coreference resolutions, eliminating the need for intermediate textual coreference resolution steps. This direct graph-to-coreference pathway prevents error propagation from text-based models while maintaining versatility in handling various coreference situations through the model's ability to learn from diverse graph structures
Data Source
AI summary
A natural language processing method, system, device, and computer readable medium using abstract meaning representation (AMR) coreference resolution. The method can include receiving an input representation, wherein the input representation can include an AMR graph. The method can further include encoding the input representation via a variational graph autoencoder (VGAE). In addition, the method can include determining one or more concept identifiers from the encoded VGAE input representation and determining one or more coreference clusters from the determined concept identifiers. In addition, the method can include determining one or more first embedding values for one or more nodes of the input representation. Further, the step of encoding the input representation can further include encoding one or more nodes of the input representation into a first representation having contextual information via a local graph encoder.