RealFormer: Transformer Likes Residual Attention

• URL: http://arxiv.org/abs/2012.11747v2
• Date: Wed, 23 Dec 2020 20:44:30 GMT
• Title: RealFormer: Transformer Likes Residual Attention
• Authors: Ruining He and Anirudh Ravula and Bhargav Kanagal and Joshua Ainslie
• Abstract summary: RealFormer is a simple Residual Attention Layer Transformer architecture. It significantly outperforms canonical Transformers on a spectrum of tasks including Masked Language Modeling, GLUE, and SQuAD.
• Score: 5.841046725396454
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transformer is the backbone of modern NLP models. In this paper, we propose RealFormer, a simple Residual Attention Layer Transformer architecture that significantly outperforms canonical Transformers on a spectrum of tasks including Masked Language Modeling, GLUE, and SQuAD. Qualitatively, RealFormer is easy to implement and requires minimal hyper-parameter tuning. It also stabilizes training and leads to models with sparser attentions. Code will be open-sourced upon paper acceptance.

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