ARCH: Efficient Adversarial Regularized Training with Caching

• URL: http://arxiv.org/abs/2109.07048v1
• Date: Wed, 15 Sep 2021 02:05:37 GMT
• Title: ARCH: Efficient Adversarial Regularized Training with Caching
• Authors: Simiao Zuo, Chen Liang, Haoming Jiang, Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen, Tuo Zhao
• Abstract summary: Adversarial regularization can improve model generalization in many natural language processing tasks. We propose a new adversarial regularization method ARCH, where perturbations are generated and cached once every several epochs. We evaluate our proposed method on a set of neural machine translation and natural language understanding tasks.
• Score: 91.74682538906691
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adversarial regularization can improve model generalization in many natural language processing tasks. However, conventional approaches are computationally expensive since they need to generate a perturbation for each sample in each epoch. We propose a new adversarial regularization method ARCH (adversarial regularization with caching), where perturbations are generated and cached once every several epochs. As caching all the perturbations imposes memory usage concerns, we adopt a K-nearest neighbors-based strategy to tackle this issue. The strategy only requires caching a small amount of perturbations, without introducing additional training time. We evaluate our proposed method on a set of neural machine translation and natural language understanding tasks. We observe that ARCH significantly eases the computational burden (saves up to 70\% of computational time in comparison with conventional approaches). More surprisingly, by reducing the variance of stochastic gradients, ARCH produces a notably better (in most of the tasks) or comparable model generalization. Our code is publicly available.

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