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Certified Robustness Against Natural Language Attacks by Causal Intervention. (arXiv:2205.12331v1 [cs.LG])
May 26, 2022, 1:20 a.m. | Haiteng Zhao, Chang Ma, Xinshuai Dong, Anh Tuan Luu, Zhi-Hong Deng, Hanwang Zhang
cs.CR updates on arXiv.org arxiv.org
Deep learning models have achieved great success in many fields, yet they are
vulnerable to adversarial examples. This paper follows a causal perspective to
look into the adversarial vulnerability and proposes Causal Intervention by
Semantic Smoothing (CISS), a novel framework towards robustness against natural
language attacks. Instead of merely fitting observational data, CISS learns
causal effects p(y|do(x)) by smoothing in the latent semantic space to make
robust predictions, which scales to deep architectures and avoids tedious
construction of noise customized …
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