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Transformer-based sequence-to-sequence architectures, while achieving state-of-the-art results on a large number of NLP tasks, can still suffer from overfitting during training.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 1901
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The vanishing gradient problem during learning recurrent neural nets and problem solutions
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We need to talk about random splits
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Announcing czeng 2.0 parallel corpus with over 2 gigawords
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Super-human performance in online low-latency recognition of conversational speech
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Better hypothesis testing for statistical machine translation: Controlling for optimizer instability
Jonathan H. Clark, Chris Dyer, Alon Lavie, and Noah A. Smith. 2011 · 2011
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Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
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Attention is all you need
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Kyle Gorman and Steven Bedrick. 2019 · 2019
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Masato Neishi and Naoki Yoshinaga. 2019 · 2019
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Proceedings of the Fifth Conference on Machine Translation . Association for Computational Linguistics, Online
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Transforming machine translation: a deep learning system reaches news translation quality comparable to human professionals
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Sentence concatenation approach to data augmentation for neural machine translation
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