KLAttack: Towards Adversarial Attack and Defense on Neural Dependency Parsing Models

Although neural language models achieve great performance on many Natural Language Processing tasks, they suffer from various adversarial attacks. Previous works mainly focus on semantic adversarial examples, which have similar semantics to the original sentences, while syntactic adversarial attacks against the dependency parsing task are still in an early stage of research. In this paper, we propose a novel method KLAttack, crafting word-level adversarial examples to attack neural-network-based dependency parsing models. Specifically, we retrieve the class probabilities from the victim dependency parsing model and compute the KL divergence by masking every word in a sentence. Then we use pre-trained language models and reference parsers to generate candidates for substitution. Experiments on the English Penn Treehank (PTB) dataset show that our method improves the attack success rate against Deep Biaffine Parser by up to 13.04% compared with previous related studies. Based on KLAttack, we further propose Syntax-Aware Transformer for Input Reconstruction, a denoiser to recover the original sentences from the adversarial examples. Trained adversarially with successfully attacked sentences from KLAttack, we enhance the robustness of the dependency parsing models by concatenating the denoiser ahead of the victim models.