  BERT adopts masked language modeling (MLM) for pre-training and is one of the
most successful pre-training models. Since BERT neglects dependency among
predicted tokens, XLNet introduces permuted language modeling (PLM) for
pre-training to address this problem. However, XLNet does not leverage the full
position information of a sentence and thus suffers from position discrepancy
between pre-training and fine-tuning. In this paper, we propose MPNet, a novel
pre-training method that inherits the advantages of BERT and XLNet and avoids
their limitations. MPNet leverages the dependency among predicted tokens
through permuted language modeling (vs. MLM in BERT), and takes auxiliary
position information as input to make the model see a full sentence and thus
reducing the position discrepancy (vs. PLM in XLNet). We pre-train MPNet on a
large-scale dataset (over 160GB text corpora) and fine-tune on a variety of
down-streaming tasks (GLUE, SQuAD, etc). Experimental results show that MPNet
outperforms MLM and PLM by a large margin, and achieves better results on these
tasks compared with previous state-of-the-art pre-trained methods (e.g., BERT,
XLNet, RoBERTa) under the same model setting. The code and the pre-trained
models are available at: https://github.com/microsoft/MPNet.
