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Status: Unchecked ×Keyword: virtual adversarial training ×Clear all

2 claims from 1 paper

1. Computer Science › Topic Modeling

   DeBERTa: Decoding-enhanced BERT with Disentangled Attention

   He, Liu, Gao and Chen · arXiv (Cornell University) · 2020

   The paper proposes DeBERTa, a language model design with two new techniques and a new fine-tuning method, and reports better pre-training efficiency and task results, including on SuperGLUE.

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   1. Unchecked“The significant performance boost makes the single DeBERTa model surpass the human performance on the SuperGLUE benchmark (Wang et al., 2019a) for the first time in terms of macro-average score (89.9 versus 89.8), and the ensemble DeBERTa model sits atop the…
   2. UncheckedA DeBERTa model trained on half the data scored higher than RoBERTa-Large on MNLI, SQuAD v2.0 and RACE, according to the paper.“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%).”