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NLP is in a period of disruptive change that is impacting our methodologies, funding sources, and public perception.
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Attention is all you need
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BERT: Pre-training of deep bidirectional transformers for language understanding
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Snowball sampling
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PyTorch: An Imperative Style, High-Performance Deep Learning Library . Curran Associates Inc., Red Hook, NY, USA
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 2019
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The affective growth of computer vision
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Should attention be all we need? the epistemic and ethical implications of unification in machine learning
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What do nlp researchers believe? results of the nlp community metasurvey
Julian Michael, Ari Holtzman, Alicia Parrish, Aaron Mueller, Alex Wang, Angelica Chen, Divyam Madaan, Nikita Nangia, Richard Yuanzhe Pang, Jason Phang, and Samuel R. Bowman. 2022 · 2022
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Geographic citation gaps in NLP research
Mukund Rungta, Janvijay Singh, Saif M. Mohammad, and Diyi Yang. 2022 · 2022
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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Nur Ahmed and Muntasir Wahed. 2020 · 2020
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Purposive sampling: complex or simple? research case examples
Steve Campbell, Melanie Greenwood, Sarah Prior, Toniele Shearer, Kerrie Walkem, Sarah Young, Danielle Bywaters, and Kim Walker. 2020 · 2020
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S2ORC: The semantic scholar open research corpus
Kyle Lo, Lucy Lu Wang, Mark Neumann, Rodney Kinney, and Daniel Weld. 2020 · 2020
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Examining citations of natural language processing literature
Saif M. Mohammad. 2020 · 2020
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What will it take to fix benchmarking in natural language understanding?
Samuel R. Bowman and George Dahl. 2021 · 2021
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The hardware lottery
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The semantic scholar academic graph (s2ag)
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The elephant in the room: Analyzing the presence of big tech in natural language processing research
Mohamed Abdalla, Jan Philip Wahle, Terry Ruas, Aurélie Névéol, Fanny Ducel, Saif M. Mohammad, and Karën Fort. 2023 · 2023
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Compute-efficient deep learning: Algorithmic trends and opportunities
Brian R. Bartoldson, Bhavya Kailkhura, and Davis Blalock. 2023 · 2023
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Ai transparency in the age of llms: A human-centered research roadmap
Q. Vera Liao and Jennifer Wortman Vaughan. 2023 · 2023
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A diachronic analysis of the nlp research paradigm shift: When, how, and why?
Aniket Pramanick, Yufang Hou, and Iryna Gurevych. 2023 · 2023
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Closed ai models make bad baselines
Anna Rogers. 2023 · 2023
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Forgotten knowledge: Examining the citational amnesia in nlp
Janvijay Singh, Mukund Rungta, Diyi Yang, and Saif M. Mohammad. 2023 · 2023
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The gradient of generative ai release: Methods and considerations
Irene Solaiman. 2023 · 2023
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Silent bugs in deep learning frameworks: An empirical study of keras and tensorflow
Florian Tambon, Amin Nikanjam, Le An, Foutse Khomh, and Giuliano Antoniol. 2023 · 2023
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