Coordinate Neural Network Functional Tag Prediction
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Solution Overview
Problem
Current semantic role labeling (SRL) implementations are inefficient in identifying functional tags within textual data, as they focus on core arguments and struggle with the flexibility and variability of functional tags, leading to reduced prediction accuracy and increased processing requirements.
Innovation Solution
The Coordinate Neural Network (CoNN) model is introduced, which treats functional tag prediction as a puzzle game, using a scheduler, encoder, and predictor to generate and refine input sequences, leveraging reinforcement learning to efficiently identify and predict functional tags by navigating the dependency tree and making predictions based on collected information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If current labeling implementations focus on identifying core arguments within input textual data, then the processing approach is simplified, but the prediction accuracy for functional tags is reduced
Solution Approach 1:
The patent segments the SRL task into two distinct components: core argument identification and functional tag prediction. By separating these tasks and applying specialized processing to each, the system achieves both processing efficiency and high prediction accuracy for functional tags, resolving the contradiction between simplified processing and accurate prediction.
2Adaptability or versatility
If current implementations use traditional SRL approaches, then the system can handle all semantic roles, but the processing requirements and complexity are increased
Solution Approach 1:
The patent extracts functional tag prediction from the traditional comprehensive SRL framework and treats it as a separate, specialized task. This extraction allows the system to maintain versatility in handling all semantic roles while reducing processing requirements by applying efficient, targeted methods specifically for functional tag identification.
3Adaptability or versatility
If traditional SRL methods are used to identify functional tags, then the system maintains a comprehensive approach, but the efficiency in identifying functional tags is reduced
Solution Approach 1:
The patent introduces dynamic, adaptive processing for functional tag prediction that adjusts to the specific characteristics of each input. By using reinforcement learning and coordinate-based approaches, the system dynamically optimizes the prediction process for functional tags while maintaining comprehensive coverage of all semantic roles, thereby improving efficiency without sacrificing versatility.
Data Source
AI summary
A computer-implemented method according to one embodiment includes receiving, at a scheduler, a training data instance and a target instance, generating, by the scheduler, an input sequence from the training data instance and the target instance, sending the input sequence from the scheduler to an encoder, mapping, by the encoder, the input sequence to a feature vector, sending the feature vector from the encoder to the scheduler, sending the feature vector from the scheduler to a predictor, and mapping, by the predictor, the feature vector to a class vector to create a label for the target instance.