【导读】BERT的出现革新了众多自然语言处理任务中的模型架构范式，由此以BERT为代表预训练语言模型（PLM）在多项任务中刷新了榜单，引起了学术界和工业界的大量关注。斯坦福大学经典自然语言处理课程2020年CS224N邀请了BERT一作、Google 研究员Jacob Devlin做了关于上下文词表示BERT以及预训练语言模型的课程讲座，亲授大厂实战经验，值得关注!
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预先训练的双向语言模型非常有效
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然而，这些模型非常昂贵
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遗憾的是，改进似乎主要来自更昂贵的模型和更多的数据
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推理/服务问题大多通过蒸馏“解决”
Jacob Devlin是谷歌的研究员。在谷歌，他的主要研究兴趣是开发用于信息检索、问题回答和其他语言理解任务的快速、强大和可扩展的深度学习模型。2014年至2017年，他在微软研究院担任首席研究员，领导微软翻译从基于短语的翻译过渡到神经机器翻译(NMT)。Devlin先生是ACL 2014年最佳长论文奖和NAACL 2012年最佳短论文奖的获得者。2009年，他在马里兰大学(University of Maryland)获得了计算机科学硕士学位，导师是邦尼·多尔(Bonnie Dorr)博士。
https://web.stanford.edu/class/cs224n/index.html#schedule
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在回复【BERT到T5】获取资源链接
内容一览
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