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XLNet: Generalized Autoregressive Pretraining for Language ...

xlnet : Generalized Autoregressive Pretrainingfor Language UnderstandingZhilin Yang 1, Zihang Dai 12, Yiming Yang1, Jaime Carbonell1,Ruslan Salakhutdinov1, Quoc V. Le21 Carnegie Mellon University,2 Google AI Brain the capability of modeling bidirectional contexts, denoising autoencodingbased Pretraining like BERT achieves better performance than Pretraining ap-proaches based on Autoregressive Language modeling. However, relying on corrupt-ing the input with masks, BERT neglects dependency between the masked positionsand suffers from a pretrain-finetune discrepancy. In light of these pros and cons, wepropose xlnet , a Generalized Autoregressive Pretraining method that (1) enableslearning bidirectional contexts by maximizing the expected likelihood over allpermutations of the factorization order and (2) overcomes the limitations of BERT thanks to its Autoregressive formulation. Furthermore, xlnet integrates ideasfrom Transformer-XL, the state-of-the-art Autoregressive model, into , under comparable experiment setting, xlnet outperforms BERT on20 tasks, often by a large margin, including question answering, natural languageinference, sentiment analysis, and document IntroductionUnsupervised representation learning has been highly successful in the domain of natural languageprocessing [7,22,27,28,10].

XLNet, a generalized autoregressive method that leverages the best of both AR language modeling and AE while avoiding their limitations. Firstly, instead of using a fixed forward or backward factorization order as in conventional AR mod- ... conditional probability p(x j^x) based on an independence assumption that all masked tokens x are ...

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