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XLNet: Generalized Autoregressive Pretraining for Language Understanding
Zhilin Yang · Zihang Dai · Yiming Yang · Jaime Carbonell · Russ Salakhutdinov · Quoc V Le

Thu Dec 12 05:00 PM -- 07:00 PM (PST) @ East Exhibition Hall B + C #141

With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling. However, relying on corrupting the input with masks, BERT neglects dependency between the masked positions and suffers from a 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 setting, XLNet outperforms BERT on 20 tasks, often by a large margin, including question answering, natural language inference, sentiment analysis, and document ranking.

Author Information

Zhilin Yang (Recurrent AI)
Zihang Dai (Carnegie Mellon University)
Yiming Yang (CMU)
Jaime Carbonell (CMU)
Russ Salakhutdinov (Carnegie Mellon University)
Quoc V Le (Google)

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