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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 1 of 1 1 ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation ... Sun, Yu; Wang, Shuohuan; Feng, Shikun; Ding, Siyu; Pang, Chao; Shang, Junyuan; Liu, Jiaxiang; Chen, Xuyi; Zhao, Yanbin; Lu, Yuxiang; Liu, Weixin; Wu, Zhihua; Gong, Weibao; Liang, Jianzhong; Shang, Zhizhou; Sun, Peng; Liu, Wei; Ouyang, Xuan; Yu, Dianhai; Tian, Hao; Wu, Hua; Wang, Haifeng. - : arXiv, 2021 Abstract: Pre-trained models have achieved state-of-the-art results in various Natural Language Processing (NLP) tasks. Recent works such as T5 and GPT-3 have shown that scaling up pre-trained language models can improve their generalization abilities. Particularly, the GPT-3 model with 175 billion parameters shows its strong task-agnostic zero-shot/few-shot learning capabilities. Despite their success, these large-scale models are trained on plain texts without introducing knowledge such as linguistic knowledge and world knowledge. In addition, most large-scale models are trained in an auto-regressive way. As a result, this kind of traditional fine-tuning approach demonstrates relatively weak performance when solving downstream language understanding tasks. In order to solve the above problems, we propose a unified framework named ERNIE 3.0 for pre-training large-scale knowledge enhanced models. It fuses auto-regressive network and auto-encoding network, so that the trained model can be easily tailored for both ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2107.02137 https://dx.doi.org/10.48550/arxiv.2107.02137 BASE Hide details

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