KBAA: an Adversarial Example Generation Method for KBQA Task

The adversarial example generation algorithm is currently a very popular algorithm for deceiving machine learning. The main method is to change the original sample in a way that is almost imperceptible to the user, and cause an obvious error in the result returned by the model. At present, there are many adversarial algorithms for computer vision, but there are few for NLP models, and there is almost no algorithm for Question & Answer task. This paper designs a framework of adversarial example generation algorithm KBAA(Knowledge-based Adversarial Attack), which is a black box attack, and is against KBQA (Knowledge-based Question & Answer). By extracting the knowledge graph of the original sample (attribute value is null, Entity-Attribute-(null)), the algorithm chooses the more important one of Entity and Attribute in the original sample and generates an adversarial example in order to deceive the KBQA model.