Neural Network Robustness via Natural Typo Injection
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Solution Overview
Problem
Current neural network models for natural language processing (NLP) are not adequately evaluated for robustness against user-generated typographical errors, as they are typically trained and tested on error-free datasets, failing to account for real-world 'noisy' texts with spelling or typographical errors.
Innovation Solution
A system and method to evaluate the robustness of NLP models by simulating natural typos, such as keyboard typos, by generating and inserting errors into input text, and analyzing the model's performance on these modified inputs to assess its resilience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If NLP models are trained and tested on error-free datasets, then training and evaluation simplicity is improved, but robustness against real-world typographical errors deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-defining typo generation rules and error patterns before the evaluation process begins. The system prepares a comprehensive set of typo transformation rules (substitutions, insertions, deletions, transpositions) in advance, then systematically applies these rules to test datasets before model evaluation, allowing robustness assessment without modifying the model training process
Solution Approach 2:
The patent implements parameter changes by systematically varying text parameters through controlled introduction of typographical errors. The system changes character-level parameters (substituting letters with adjacent keyboard characters), word-level parameters (inserting, deleting, or transposing characters), and sentence-level parameters (applying multiple typo types组合), thereby evaluating model robustness across different error conditions while maintaining the original clean dataset for comparison
2Measurement precision
If NLP models are evaluated on noisy texts with typographical errors, then robustness assessment accuracy is improved, but evaluation complexity increases
Solution Approach 1:
The patent applies segmentation by breaking down the complex evaluation process into distinct modular components: (1) typo rule definition module that categorizes error types, (2) text transformation module that applies specific typo patterns, (3) model evaluation module that assesses performance on modified texts, and (4) results analysis module that compares clean vs. noisy text performance. This segmentation allows systematic robustness assessment while managing evaluation complexity through organized, reusable components
Solution Approach 2:
The patent introduces an intermediary typo generation system that acts as a mediator between the clean test dataset and the NLP model evaluation. This intermediary component systematically transforms clean texts into noisy versions according to predefined rules, then feeds both original and modified texts to the model for comparative evaluation, thereby enabling precise robustness measurement without directly complicating the core model architecture
3Adaptability or versatility
If typographical errors are introduced into input text, then real-world applicability of evaluation is improved, but input text quality deteriorates
Solution Approach 1:
The patent implements dynamics by creating a flexible, configurable typo generation system that can dynamically adjust the type, frequency, and severity of introduced errors based on evaluation needs. The system allows dynamic selection among different typo categories (keyboard-adjacent substitutions, random character replacements, insertions, deletions, transpositions) and can adapt the error injection rate to match different real-world scenarios, thereby maintaining input text quality control while improving real-world applicability
Data Source
AI summary
A method for evaluating robustness of one or more target neural network models using natural typos. The method includes receiving one or more natural typo generation rules associated with a first task associated with a first input document type, receiving a first target neural network model, and receiving a first document and corresponding its ground truth labels. The method further includes generating one or more natural typos for the first document based on the one or more natural typo generation rules, and providing, to the first target neural network model, a test document generated based on the first document and the one or more natural typos as an input document to generate a first output. A robustness evaluation result of the first target neural network model is generated based on a comparison between the output and the ground truth labels.