2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDHLT-14.1
Paper Title ENHANCING MODEL ROBUSTNESS BY INCORPORATING ADVERSARIAL KNOWLEDGE INTO SEMANTIC REPRESENTATION
Authors Jinfeng Li, Alibaba Group, China; Tianyu Du, Zhejiang University, China; Xiangyu Liu, Rong Zhang, Hui Xue, Alibaba Group, China; Shouling Ji, Zhejiang University, China
SessionHLT-14: Language Representations
LocationGather.Town
Session Time:Thursday, 10 June, 14:00 - 14:45
Presentation Time:Thursday, 10 June, 14:00 - 14:45
Presentation Poster
Topic Human Language Technology: [HLT-MLMD] Machine Learning Methods for Language
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Despite that deep neural networks (DNNs) have achieved enormous success in many domains like natural language processing (NLP), they have also been proven to be vulnerable to maliciously generated adversarial examples. Such inherent vulnerability has threatened various real-world deployed DNNs-based applications. To strength the model robustness, several countermeasures have been proposed in the English NLP domain and obtained satisfactory performance. However, due to the unique language properties of Chinese, it is not trivial to extend existing defenses to the Chinese domain. Therefore, we propose AdvGraph, a novel defense which enhances the robustness of Chinese-based NLP models by incorporating adversarial knowledge into the semantic representation of the input. Extensive experiments on two real-world tasks show that AdvGraph exhibits better performance compared with previous work: (i) effective -- it significantly strengthens the model robustness even under the adaptive attacks setting without negative impact on model performance over legitimate input; (ii) generic -- its key component, i.e., the representation of connotative adversarial knowledge is task-agnostic, which can be reused in any Chinese-based NLP models without retraining; and (iii) efficient -- it is a light-weight defense with sub-linear computational complexity, which can guarantee the efficiency required in practical scenarios.