12. Červenec 2020
Deep neural networks for natural language processing tasks are vulnerable to adversarial input perturbations. Existing works have proposed to improve the robustness against specific adversarial input perturbations (e.g., token substitutions), but do not consider general perturbations such as token insertions, token deletions, token swaps, etc. To fill this gap, we present a technique to train models that are robust to user-defined string transformations. Our technique combines data augmentation—to detect worst-case transformed inputs—and verifiable training using abstract interpretation—to further increase the robustness of the model on the worst-case transformed inputs. We use our technique to train models on the AG and SST2 datasets and show that the resulting models are robust to combinations of user-defined transformations mimicking spelling mistakes and other meaning-preserving transformations.
The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.
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