DPAEG: A Dependency Parse-Based Adversarial Examples Generation Method for Intelligent Q&A Robots

Abstract EN

Recently, the natural language processing- (NLP-) based intelligent question and answer (Q&A) robots have been used ubiquitously.

However, the robustness and security of current Q&A robots are still unsatisfactory, e.g., a slight typo in the user’s question may cause the Q&A robot unable to return the correct answer.

In this paper, we propose a fast and automatic test dataset generation method for the robustness and security evaluation of current Q&A robots, which can work in black-box scenarios and thus can be applied to a variety of different Q&A robots.

Specifically, we propose a dependency parse-based adversarial examples generation (DPAEG) method for Q&A robots.

DPAEG first uses the proposed dependency parse-based keywords extraction algorithm to extract keywords from a question.

Then, the proposed algorithm generates adversarial words according to the extracted keywords, which include typos and words that are spelled similarly to the keywords.

Finally, these adversarial words are used to generate a large number of adversarial questions.

The generated adversarial questions which are similar to the original questions do not affect human’s understanding, but the Q&A robots cannot answer these adversarial questions correctly.

Moreover, the proposed method works in a black-box scenario, which means it does not need the knowledge of the target Q&A robots.

Experiment results show that the generated adversarial examples have a high success rate on two state-of-the-art Q&A robots, DrQA and Google Assistant.

In addition, the generated adversarial examples not only affect the correct answer (top-1) returned by DrQA but also affect the top-k candidate answers returned by DrQA.

The adversarial examples make the top-k candidate answers contain fewer correct answers and make the correct answers rank lower in the top-k candidate answers.

The human evaluation results show that participants with different genders, ages, and mother tongues can understand the meaning of most of the generated adversarial examples, which means that the generated adversarial examples do not affect human’s understanding.