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Biomedical research papers use significantly different language and jargon when compared to typical English text, which reduces the utility of pre-trained NLP models in this domain.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
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A neural probabilistic language model
Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Jauvin · 2003
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Overview of biocreative: critical assessment of information extraction for biology, 2005
Lynette Hirschman, Alexander Yeh, Christian Blaschke, and Alfonso Valencia · 2005
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Scigen-an automatic cs paper generator, 2005
Jeremy Stribling, Max Krohn, and Dan Aguayo · 2005
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An information-theoretic approach to automatic evaluation of summaries
Chin-Yew Lin, Guihong Cao, Jianfeng Gao, and Jian-Yun Nie · 2006
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Meteor: An automatic metric for mt evaluation with high levels of correlation with human judgments
Alon Lavie and Abhaya Agarwal · 2007
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Literature-based discovery
Peter Bruza and Marc Weeber · 2008
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The extent and consequences of p-hacking in science
Megan L Head, Luke Holman, Rob Lanfear, Andrew T Kahn, and Michael D Jennions · 2015
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Accelerating Discovery: Mining Unstructured Information for Hypothesis Generation
Scott Spangler · 2015
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Cider: Consensus-based image description evaluation
Ramakrishna Vedantam, C Lawrence Zitnick, and Devi Parikh · 2015
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Spice: Semantic propositional image caption evaluation
Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould · 2016
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The spreading of misinformation online
Michela Del Vicario, Alessandro Bessi, Fabiana Zollo, Fabio Petroni, Antonio Scala, Guido Caldarelli, H Eugene Stanley, and Walter Quattrociocchi · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al · 2016
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Image captioning with semantic attention
Quanzeng You, Hailin Jin, Zhaowen Wang, Chen Fang, and Jiebo Luo · 2016
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Toward controlled generation of text
Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, and Eric P Xing · 2017
Cited alongside, same era.
Shikhar Sharma, Layla El Asri, Hannes Schulz, and Jeremie Zumer · 2017
Cited alongside, same era.
Moliere: Automatic biomedical hypothesis generation system
Justin Sybrandt, Michael Shtutman, and Ilya Safro · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Artificial intelligence in neurodegenerative disease research: use of ibm watson to identify additional rna-binding proteins altered in amyotrophic lateral sclerosis
Nadine Bakkar, Tina Kovalik, Ileana Lorenzini, Scott Spangler, Alix Lacoste, Kyle Sponaugle, Philip Ferrante, Elenee Argentinis, Rita Sattler, and Robert Bowser · 2018
Cited alongside, same era.
Pytorch lightning
W.A. et al. Falcon · 2019
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Ctrl: A conditional transformer language model for controllable generation
Nitish Shirish Keskar, Bryan McCann, Lav R Varshney, Caiming Xiong, and Richard Socher · 2019
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Biobert: pre-trained biomedical language representation model for biomedical text mining
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang · 2019
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When is it appropriate to publish high-stakes ai research
Claire Leibowicz, Steven Adler, and Peter Eckersley · 2019
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
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Lies, line drawing, and deep fake news
Marc Jonathan Blitz · 2018
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Artificial intelligence, deepfakes and a future of ectypes
Luciano Floridi · 2018
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Subword regularization: Improving neural network translation models with multiple subword candidates
Taku Kudo · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever · 2018
Cited alongside, same era.
Glue: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman · 2018
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Inhibition of the dead box rna helicase 3 prevents hiv-1 tat and cocaine-induced neurotoxicity by targeting microglia activation
Marina Aksenova, Justin Sybrandt, Biyun Cui, Vitali Sikirzhytski, Hao Ji, Diana Odhiambo, Matthew D Lucius, Jill R Turner, Eugenia Broude, Edsel Peña, et al · 2019
Cited alongside, same era.
Scibert: Pretrained language model for scientific text
Iz Beltagy, Kyle Lo, and Arman Cohan · 2019
Cited alongside, same era.
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Scispacy: Fast and robust models for biomedical natural language processing
Mark Neumann, Daniel King, Iz Beltagy, and Waleed Ammar · 2019
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Transfer learning in biomedical natural language processing: An evaluation of bert and elmo on ten benchmarking datasets
Yifan Peng, Shankai Yan, and Zhiyong Lu · 2019
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Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander H Miller, and Sebastian Riedel · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2019
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Ernie: Enhanced representation through knowledge integration
Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Xuyi Chen, Han Zhang, Xin Tian, Danxiang Zhu, Hao Tian, and Hua Wu · 2019
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Bert has a mouth, and it must speak: Bert as a markov random field language model
Alex Wang and Kyunghyun Cho · 2019
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Large batch optimization for deep learning: Training bert in 76 minutes
Yang You, Jing Li, Sashank Reddi, Jonathan Hseu, Sanjiv Kumar, Srinadh Bhojanapalli, Xiaodan Song, James Demmel, and Cho-Jui Hsieh · 2019
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