pretrain-finetune discrepancy. In light of these pros and cons, we propose XLNet, a generalized autoregressive
pretraining method that (1) enables learning bidirectional contexts by maximizing the expected likelihood over all
permutations of the factorization order and (2) overcomes the limitations of BERT thanks to its autoregressive
formulation. Furthermore, XLNet integrates ideas from Transformer-XL, the state-of-the-art autoregressive model, into
pretraining. Empirically, under comparable experiment settings, XLNet outperforms BERT on 20 tasks, often by a large
margin, including question answering, natural language inference, sentiment analysis, and document ranking.
Tips:
The specific attention pattern can be controlled at training and test time using the perm_mask input.
Due to the difficulty of training a fully auto-regressive model over various factorization order, XLNet is pretrained
