Question-Answer Dialog System Adversarial Defense Training
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Solution Overview
Problem
Question-answer dialog systems are vulnerable to adversarial attacks, which corrupt their responses and affect their ability to provide accurate answers, especially in multilingual contexts where they lack robustness against malicious inputs.
Innovation Solution
The method generates adversarial statements by converting questions into statements with placeholders, randomly selecting entities from a training corpus to replace the placeholders, and inserting these statements into the context to train the dialog system, thereby reinforcing its ability to recognize and defend against adversarial attacks through bootstrapping adversarial policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If adversarial statements are inserted into the context to train the dialog system, then the robustness against adversarial attacks is improved, but the training data complexity and processing difficulty increase
Solution Approach 1:
The system performs preliminary actions by generating adversarial statements in advance and inserting them into training contexts before the actual training process. This allows the dialog system to be pre-exposed to adversarial patterns, enabling it to learn defense mechanisms proactively rather than reactively, thereby improving robustness while managing complexity through structured pre-processing
Solution Approach 2:
The system converts harmful adversarial statements into beneficial training examples. By intentionally introducing adversarial content into the training data, the system transforms potential threats into learning opportunities, enabling the dialog system to recognize and resist adversarial attacks through repeated exposure and learning from corrected examples
2Adaptability or versatility
If the dialog system is trained to recognize adversarial attacks across multiple languages, then the adaptability to multilingual contexts is improved, but the training time and computational resources increase
Solution Approach 1:
The system implements a universal training approach where adversarial statement generation and insertion mechanisms work across multiple languages simultaneously. The same core processing pipeline handles different languages, making the system multilingual-capable without requiring separate training systems for each language, thus improving adaptability while controlling computational overhead through resource sharing
Solution Approach 2:
The system changes training parameters such as adversarial statement insertion probability, context length, and language sampling ratios to optimize training efficiency. By dynamically adjusting these parameters during training, the system can balance between comprehensive multilingual coverage and training time constraints, achieving good adaptability without excessive computational cost
Data Source
AI summary
A method, computer program product, and/or computer system generate a first adversarial statement via: (1) receiving a question and an original context for the question; (2) converting the question into a statement with a placeholder answer; (3) picking randomly an answer entity from a training text corpus; (4) replacing the placeholder answer with the randomly picked answer entity; and (5) leaving a correct question entity in the statement. The first adversarial statement is inserted into the original context to form a first adversarial context. The question and the first adversarial context as a first pair and the question and the original context as a second pair are input into a question-answer dialog system to train the question-answer dialog system.