VGAE Pretraining for Resource-Efficient AMR Coreference Resolution
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Solution Overview
Problem
Existing methods for abstract meaning representation (AMR) coreference resolution are resource-intensive, costly, and prone to errors, particularly when handling multi-sentence texts, due to reliance on string matches and pipeline systems that suffer from severe error propagation.
Innovation Solution
A variational graph autoencoder (VGAE) is employed for pretraining AMR coreference resolution, leveraging existing supervision signals and automatically parsing AMR data to minimize errors and resource utilization, using reconstruction loss and variational restrictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional text-based coreference models with GNN encoders are used for AMR coreference resolution, then performance improves over rule-based methods, but data and computing resources become intensive and costly
Solution Approach 1:
The patent applies preliminary action by pre-training the VGAE model on graph reconstruction tasks before fine-tuning for coreference resolution. This pre-training phase allows the model to learn effective graph representations and coreference patterns from available AMR data, reducing the amount of task-specific annotated data needed and lowering overall computing resource requirements while maintaining high accuracy
2Adaptability or versatility
If pipeline systems with textual coreference resolution models and text-to-AMR aligners are used, then many coreference situations can be resolved, but severe error propagation occurs
Solution Approach 1:
The patent merges the coreference resolution task directly into the AMR generation process by using a unified VGAE model that operates on graph structures. This integration eliminates the separate pipeline stages (textual coreference resolution + text-to-AMR alignment) that caused error propagation, allowing the model to resolve coreference situations while generating AMRs in a single coherent process, thereby maintaining both versatility and reliability
3Ease of manufacture
If string matching methods are used to detect coreference, then implementation is simple, but errors occur when concepts with same surface string do not point to same entity
Solution Approach 1:
The patent replaces the mechanical string matching system with a variational graph autoencoder that processes AMR graph structures. Instead of comparing surface strings, the VGAE model learns to match concepts based on their semantic roles, relationships, and contextual information encoded in the graph structure, thereby eliminating false positives from string matching while maintaining implementation feasibility through automated training
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.