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  "documentTitle": "2019 Air Street Capital The State of AI Report 2019",
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      "text": "Pretraining models to learn high- and low-level features has been transformative in computer vision. In the last year there have been similar empirical breakthroughs in pretraining language models on large text corpora to learn high- and low-level language features.",
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      "text": "Unlike ImageNet, these language models are typically trained on very large amounts of publicly available, i.e. unlabeled text from the web. This method could be further scaled up to generate gains in NLP tasks and unlock many new commercial applications in the same way that transfer learning from ImageNet has driven more industrial uses of computer vision.",
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      "text": "Various research breakthroughs (Google AI's BERT, Transformer; Allen Institute's ELMo; OpenAI's Transformer, Ruder & Howard's ULMFiT, Microsoft's MT-DNN) demonstrated that pretrained language models can substantially improve performance on a variety of NLP tasks.",
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      "text": "*Kitaev and Klein, ACL 2018 (see also Joshi et al., ACL 2018) The improvements ELMo achieved on a wide range of NLP tasks. (Source: Matthew Peters)",
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