Dialog Robustness Analysis Using Controlled Adversarial Samples
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Solution Overview
Problem
Existing methods for evaluating the robustness of dialog understanding models in natural language processing yield inaccurate results due to significant differences in test samples before and after transformation, leading to unreliable assessments.
Innovation Solution
A method involving the reconstruction of original dialog samples into adversarial samples with perturbations at various levels (character, word, sentence) to form an adversarial sample set, followed by testing with both original and adversarial sets to determine robustness based on evaluation data changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If transformation is performed on test samples to evaluate model robustness, then the evaluation process can be completed, but the significant differences between transformed and original samples lead to inaccurate evaluation results
Solution Approach 1:
The patent applies parameter changes by introducing a control parameter λ that determines the degree of sample transformation. By adjusting λ between 0 and 1, the system can control the balance between original and transformed sample characteristics, thereby achieving accurate robustness evaluation while maintaining sample consistency.
Solution Approach 2:
The patent implements dynamics by creating a dynamic blending mechanism where the test sample is constructed as a combination of original and transformed samples with adjustable weights. This dynamic approach allows the evaluation process to adapt the transformation intensity based on evaluation needs, resolving the contradiction between completing transformation-based evaluation and maintaining sample consistency.
2Object-affected harmful factors
If significant transformation is applied to test samples, then the adversarial attack effect is enhanced, but the difference from original samples increases leading to unreliable assessment
Solution Approach 1:
The patent uses parameter changes by introducing a controllable transformation strength parameter that regulates the intensity of adversarial perturbations. This allows the system to adjust the harmful factor level to achieve effective adversarial attacks while maintaining sufficient similarity to original samples for reliable assessment.
Solution Approach 2:
The patent implements feedback mechanisms by using the original sample characteristics as a reference baseline during the transformation process. The evaluation system continuously compares transformed samples against original samples and adjusts transformation intensity based on the observed differences, ensuring that adversarial effectiveness is achieved without compromising assessment reliability.
Data Source
AI summary
An original sample set is acquired, the original sample set includes a plurality of original dialog samples, and each original dialog sample includes a round of dialog having at least two speaking turns from different speakers. The plurality of original dialog samples are reconstructed to obtain at least an adversarial sample set associated with a perturbation attack scope, each original dialog sample is modified according to the perturbation attack scope to reconstruct a modified dialog sample in the adversarial sample set. A first test of a dialog understanding model is performed by using the original sample set to obtain original evaluation data. A second test of the dialog understanding model is performed by using the adversarial sample set to obtain adversarial evaluation data. A robustness analysis result is determined according to a change of the adversarial evaluation data with respect to the original evaluation data.