before_revision: Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks. In this paper we propose a new model architecture DeBERTa(Decoding-enhanced BERT with disentangled attention) that improves the BERT and RoBERTa models using two novel techniques. The first is the disentangled attention mechanism, where each word is represented using two vectors that encode its content and position, respectively, and the attention weights among words are computed using disentangled matrices on their contents and relative positions. Second, an enhanced mask decoder is used to replace the output softmax layer to predict the masked tokens for model pretraining . We show that these two techniques significantly improve the efficiency of model pre-training and performance of downstream tasks. Compared to RoBERTa-Large, a DeBERTa model trained on half of the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9\% (90.2\% vs. 91.1\%), on SQuAD v2.0 by +2.3\% (88.4\% vs. 90.7\%) and RACE by +3.6\% (83.2\% vs. 86.8\%). The DeBERTa code and pre-trained models will be made publicly available at URL
after_revision: Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks. In this paper wepropose a new model architecture DeBERTa(Decoding-enhanced BERT with dis-entangled attention) that improves the BERT and RoBERTa models using two novel techniques. The first is the disentangled attention mechanism, where each word is represented using two vectors that encode its content and position, respectively, and the attention weights among words are computed using disentangled matrices on their contents and relative positions. Second, an enhanced mask decoder is used to incorporate absolute positions in the decoding layer to predict the masked tokens in model pre-training . We show that these two techniques significantly improve the efficiency of model pre-training and the performance of both natural languageunderstand (NLU) and natural langauge generation (NLG) tasks. Compared to RoBERTa-Large, a DeBERTa model trained on half of the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9\% (90.2\% vs. 91.1\%), on SQuAD v2.0 by +2.3\% (88.4\% vs. 90.7\%) and RACE by +3.6\% (83.2\% vs. 86.8\%). Notably, we scale up DeBERTa to 1.5 billion parameters and it substantially outperforms Google's T5 with 11 billionparameters on the SuperGLUE benchmark (Wang et al., 2019a) and, for the first time, surpasses the human performance (89.9 vs. 89.8).
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edit_actions: The DeBERTa code and pre-trained models will be made publicly available at URL
edit_actions: Notably, we scale up DeBERTa to 1.5 billion parameters and it substantially outperforms Google's T5 with 11 billionparameters on the SuperGLUE benchmark (Wang et al., 2019a) and, for the first time, surpasses the human performance (89.9 vs. 89.8).
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