What is MPNet and How Does it Work?
MPNet is a pre-training method for language models that combines two approaches, masked language modeling (MLM) and permuted language modeling (PLM), to create a more efficient and effective model. It was designed to address the issues of two other popular pre-training models, BERT and XLNet. MPNet takes into consideration the dependency among predicted tokens and alleviates the position discrepancy of XLNet by utilizing the position information of all tokens as input.
The Training Objective of MPNet
The training objective of MPNet is:
$$ \mathbb{E}\_{z\in{\mathcal{Z}\_{n}}} \sum^{n}\_{t=c+1}\log{P}\left(x\_{z\_{t}}\mid{x\_{z\_{}{c}}}; \theta\right) $$
This training objective is more complex than those of other pre-training models because MPNet considers the tokens preceding the current predicted token rather than just non-predicted tokens. Additionally, MPNet takes more information by using the mask symbol $[M]$ in position $z\_{>c}$ as input. While the objective may be simple in theory, it is challenging to implement efficiently for practical use.
The Advantages of MPNet
MPNet has many benefits compared to other pre-training models. One of the biggest advantages is its ability to take into consideration the dependency among predicted tokens. This allows the model to make more accurate predictions and improve its overall performance. Additionally, by utilizing the positional information of all tokens, MPNet can alleviate the issue of position discrepancy in other pre-training models such as XLNet.
Overall, MPNet is a promising pre-training method for language models that has the potential to greatly improve the accuracy and efficiency of natural language processing.
