XLNet

The authors note that bidirectional models with denoising autoencoding pretraining (such as BERT) achieve better performance compared to unidirectional autoregressive models (such as transformer-XL). But as we mentioned in the Pretraining subsection of the Bidirectional encoder representations from transformers section, the [MASK] token introduces a discrepancy between the pretraining and fine-tuning steps. To overcome these limitations, the authors of transformer-XL propose XLNet (see XLNet: Generalized Autoregressive Pretraining for Language Understanding at https://arxiv.org/abs/1906.08237): a generalized autoregressive pretraining mechanism that enables learning bidirectional contexts by maximizing the expected likelihood over all ...

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