Adaptive Curriculum Learning for Relation Extraction Models
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Solution Overview
Problem
Supervised deep learning models for relation extraction require large amounts of manually labeled data, making it difficult to generalize well, especially when domain-specific datasets are not available, and existing data augmentation techniques are not effective for text due to its discrete nature.
Innovation Solution
The method involves using dependency parsing and constituency parsing to generate augmented training data while preserving entity mentions and relationships, and lexically constrained paraphrasing to create new examples, combined with an adaptive curriculum learning process to order and train the model effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If supervised deep learning models are trained on manually labeled data, then model performance improves, but the cost and time required for data annotation increases significantly
Solution Approach 1:
The patent applies preliminary action by using dependency parsing and constituency parsing to pre-process and structure the text data before model training. These parsing operations are performed in advance to create augmented training examples, reducing the need for extensive manual annotation while maintaining model performance.
Solution Approach 2:
The patent employs copying by generating synthetic training examples through parsing operations. Instead of manually creating new labeled data, the system creates copies and transformations of existing data structures (dependency trees, constituency trees) to generate additional training samples, thereby reducing annotation time while preserving model performance.
2Ease of manufacture
If a small amount of training data is used, then annotation cost decreases, but the model overfits and fails to generalize well
Solution Approach 1:
The patent applies segmentation by breaking down sentences into their constituent grammatical structures through dependency parsing and constituency parsing. This segmentation transforms a small amount of raw text into multiple structured training examples, effectively increasing the training data volume without requiring additional manual annotation, thereby improving model generalization.
Solution Approach 2:
The patent uses parameter changes by transforming the representation of training data from raw text to structured parse trees. By changing the parameterization of the data (from sequences of words to hierarchical tree structures), the system extracts more information from the same amount of text, improving model generalization without increasing annotation effort.
3Ease of operation
If data augmentation is applied to text using simple operations like flipping or rotating, then the process becomes simple, but these operations are ineffective for text due to its discrete nature
Solution Approach 1:
The patent replaces mechanical operations (flipping, rotating) with linguistic operations (dependency parsing, constituency parsing, lexical substitution). Instead of applying physical transformations inappropriate for text, the system uses language-specific processing to generate augmented examples, maintaining both simplicity and effectiveness.
Solution Approach 2:
The patent introduces parsing structures (dependency trees, constituency trees) as intermediaries between the original text and the augmented examples. These intermediary representations capture the grammatical and semantic structure of text, enabling effective augmentation operations that respect the discrete nature of language while maintaining simplicity.
Data Source
AI summary
Methods for training a relation extraction model include using dependency parsing, constituency parsing, and lexically constrained paraphrasing to augment the training data for the model. Adaptive curriculum learning is used to train the model using the augmented training data such that different scoring functions are used at different levels of training to order the training data for the curriculum learning.