Not All Attention Is All You Need
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Self-attention based models have achieved remarkable success in natural language processing. However, the self-attention network design is questioned as suboptimal in recent studies, due to its veiled validity and high redundancy. In this paper, we focus on pre-trained language models with self-pruning training design on task-specific tuning. We demonstrate that the lighter state-of-the-art models with nearly 80 pruned, may achieve even better results on multiple tasks, including natural language understanding, document classification, named entity recognition and POS tagging, with nearly twice faster inference.
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