DeBERTa: Decoding-enhanced BERT with Disentangled Attention

DeBERTa improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of NLU tasks with 80GB training data.

Please check the official repository for more details and updates.

Fine-tuning on NLU tasks

We present the dev results on SQuAD 1.1/2.0 and MNLI tasks.

Model SQuAD 1.1 SQuAD 2.0 MNLI-m
RoBERTa-base 91.5/84.6 83.7/80.5 87.6
XLNet-Large -/- -/80.2 86.8
DeBERTa-base 93.1/87.2 86.2/83.1 88.8

Citation

If you find DeBERTa useful for your work, please cite the following paper:

@misc{he2020deberta,
    title={DeBERTa: Decoding-enhanced BERT with Disentangled Attention},
    author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen},
    year={2020},
    eprint={2006.03654},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
        }
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