Parameterized Neighborhood Memory Adaptation for Semantic Role Labeling
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Solution Overview
Problem
Deep learning models for semantic role labeling struggle with low-frequency exceptions, where the semantic role of a token is heavily context-dependent and occurs infrequently in the training dataset, leading to inaccurate predictions.
Innovation Solution
The implementation of parameterized neighborhood memory adaptation (PNMA) involves generating a library of token activation maps and using learnable neighborhood parameters to combine nearest neighbors, allowing the model to better handle low-frequency exceptions by updating the parameters through backpropagation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep learning models are trained on standard training datasets, then they can handle common semantic role labeling cases, but they fail to accurately predict low-frequency exceptions that are heavily context-dependent
Solution Approach 1:
The system pre-computes and stores activation maps from the encoder for all training tokens before the labeling phase. These pre-computed activation maps serve as a reference library that enables the model to quickly compare and adapt to new context-dependent cases without retraining the entire model, thus improving reliability for low-frequency exceptions.
Solution Approach 2:
The system creates a copy of the encoder's activation maps and stores them in a separate reference structure. This copied activation data can be queried and compared against new inputs without modifying the original model weights, allowing the system to adapt to context-dependent variations while preserving the base model's generalization capabilities.
2Adaptability or versatility
If the model uses traditional training approaches, then training is computationally efficient, but the model cannot adapt to context-dependent low-frequency semantic roles
Solution Approach 1:
The system separates the semantic role labeling process into distinct components: the base encoder model, the activation map storage structure, and the similarity comparison mechanism. This segmentation allows each component to be optimized independently, managing complexity while enabling context-dependent adaptation through the additional activation map reference layer.
Solution Approach 2:
The system introduces an intermediary activation map reference structure that mediates between the base encoder model and the final labeling decision. This intermediary layer enables context-dependent adaptation by providing reference activation patterns without requiring direct modification of the base model, thus managing complexity while improving versatility.
3Measurement precision
If neighborhood parameters are learned through backpropagation, then the model achieves high accuracy on low-frequency exceptions, but the training process becomes more computationally intensive
Solution Approach 1:
The system pre-computes activation maps and stores them in a reference structure before the actual training and prediction phases. This preliminary action eliminates the need for repeated forward passes through the encoder during training, reducing computational energy requirements while maintaining the ability to achieve high precision through parameter learning.
Solution Approach 2:
The system uses the pre-computed activation maps to serve as self-referential training data. During training, the model compares activation patterns against this self-generated reference library, reducing the need for external computational resources and enabling efficient learning of neighborhood parameters for improved semantic role identification accuracy.
Data Source
AI summary
Systems and techniques that facilitate parameterized neighborhood memory adaptation for semantic role labeling are provided. In various embodiments, a system can comprise a receiver component that can access a semantic role labeling model trained on a training dataset. In various aspects, the system can further comprise an execution component that can retrain a labeler of the semantic role labeling model based on a set of neighborhood parameters learned from the training dataset. In various instances, the execution component can execute, after retraining, the semantic role labeling model on an inputted sentence.