UniDrop: A Simple yet Effective Technique to Improve Transformer without Extra Cost

@inproceedings{Wu2021UniDropAS,
  title={UniDrop: A Simple yet Effective Technique to Improve Transformer without Extra Cost},
  author={Zhen Wu and Lijun Wu and Qi Meng and Yingce Xia and Shufang Xie and Tao Qin and Xinyu Dai and Tie-Yan Liu},
  booktitle={North American Chapter of the Association for Computational Linguistics},
  year={2021}
}
Transformer architecture achieves great success in abundant natural language processing tasks. The over-parameterization of the Transformer model has motivated plenty of works to alleviate its overfitting for superior performances. With some explorations, we find simple techniques such as dropout, can greatly boost model performance with a careful design. Therefore, in this paper, we integrate different dropout techniques into the training of Transformer models. Specifically, we propose an… 

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