  This paper presents a new pre-trained language model, DeBERTaV3, which
improves the original DeBERTa model by replacing mask language modeling (MLM)
with replaced token detection (RTD), a more sample-efficient pre-training task.
Our analysis shows that vanilla embedding sharing in ELECTRA hurts training
efficiency and model performance. This is because the training losses of the
discriminator and the generator pull token embeddings in different directions,
creating the "tug-of-war" dynamics. We thus propose a new gradient-disentangled
embedding sharing method that avoids the tug-of-war dynamics, improving both
training efficiency and the quality of the pre-trained model. We have
pre-trained DeBERTaV3 using the same settings as DeBERTa to demonstrate its
exceptional performance on a wide range of downstream natural language
understanding (NLU) tasks. Taking the GLUE benchmark with eight tasks as an
example, the DeBERTaV3 Large model achieves a 91.37% average score, which is
1.37% over DeBERTa and 1.91% over ELECTRA, setting a new state-of-the-art
(SOTA) among the models with a similar structure. Furthermore, we have
pre-trained a multi-lingual model mDeBERTa and observed a larger improvement
over strong baselines compared to English models. For example, the mDeBERTa
Base achieves a 79.8% zero-shot cross-lingual accuracy on XNLI and a 3.6%
improvement over XLM-R Base, creating a new SOTA on this benchmark. We have
made our pre-trained models and inference code publicly available at
https://github.com/microsoft/DeBERTa.
