Related papers: Adversarial Self-Attention for Language Understanding

Adversarial Self-Attention for Language Understanding

URL: http://arxiv.org/abs/2206.12608v1
Date: Sat, 25 Jun 2022 09:18:10 GMT
Title: Adversarial Self-Attention for Language Understanding
Authors: Hongqiu Wu and Hai Zhao
Abstract summary: This paper proposes textitAdversarial Self-Attention mechanism (ASA). ASA adversarially reconstructs the Transformer attentions and facilitates model training from contaminated model structures. For fine-tuning, ASA-empowered models consistently outweigh naive models by a large margin considering both generalization and robustness.
Score: 89.265747130584
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: An ultimate language system aims at the high generalization and robustness when adapting to diverse scenarios. Unfortunately, the recent white hope pre-trained language models (PrLMs) barely escape from stacking excessive parameters to the over-parameterized Transformer architecture to achieve higher performances. This paper thus proposes \textit{Adversarial Self-Attention} mechanism (ASA), which adversarially reconstructs the Transformer attentions and facilitates model training from contaminated model structures, coupled with a fast and simple implementation for better PrLM building. We conduct comprehensive evaluation across a wide range of tasks on both pre-training and fine-tuning stages. For pre-training, ASA unfolds remarkable performance gain compared to regular training for longer periods. For fine-tuning, ASA-empowered models consistently outweigh naive models by a large margin considering both generalization and robustness.

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