DeBERTa: Decoding-enhanced BERT with Disentangled Attention
DeBERTa
improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
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:
@inproceedings{
he2021deberta,
title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION},
author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=XPZIaotutsD}
}
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