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1 Introduction 1 2 Proposed Method 2 2.1 Background 2 2.2 Objective: Permutation Language Modeling 3 2.3 Architecture: Two-Stream Self-Attention for Target-Aware Representations 4 2.4 Incorporating Ideas from Transformer-XL 5 2.5 Modeling Multiple Segments 5 2.6 Discussion 6 3 Experiments 6 3.1 Pretraining and Implementation 6 3.2 Fair Comparison with BERT 7 3.3 Comparison with RoBERTa: Scaling Up 7 3.4 Ablation Study 8 4 Conclusions 9 A Target-Aware Representation via Two-Stream Self-Attention 12 A.1 A Concrete Example of How Standard LM Parameterization Fails 12 A.2 Two-Stream Attention 12 A.3 Datasets 12 A.3.1 RACE Dataset 12 A.3.2 SQuAD 12 A.3.3 Text classification Datasets 13 A.3.4 GLUE Dataset 13 A.3.5 ClueWeb09-B Dataset 13 A.4 Hyperparameters 13 A.4.1 Pretraining Hyperparameters 13 A.4.2 Hyperparameters for Finetuning 13 A.5 Discussion and Analysis 14 A.5.1 Comparison with BERT 14 A.5.2 Comparison with Language Modeling 14 A.5.3 Bridging the Gap Between Language Modeling and Pretraining 15 A.6 Qualitative Analysis of Attention Patterns 15 A.7 Visualizing Memory and Permutation 16

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