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Pre-trained language models such as ClinicalBERT have achieved impressive results on tasks such as medical Natural Language Inference.
On adversarial examples for biomedical NLP tasks
Vladimir Araujo, Andres Carvallo, Carlos Aspillaga, and Denis Parra. 2020 · 2004
Earlier work this paper cites.
Snomed-ct: The advanced terminology and coding system for ehealth
Kevin Donnelly et al. 2006 · 2006
Earlier work this paper cites.
George Michalopoulos, Yuanxin Wang, Hussam Kaka, Helen Chen, and Alex Wong. 2020 · 2010
Earlier work this paper cites.
Autoprompt: Eliciting knowledge from language models with automatically generated prompts
Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2010
Earlier work this paper cites.
Mimic-iii, a freely accessible critical care database
Alistair EW Johnson, Tom J Pollard, Lu Shen, H Lehman Li-Wei, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. 2016 · 2016
Earlier work this paper cites.
The lambada dataset: Word prediction requiring a broad discourse context
Denis Paperno, Germán Kruszewski, Angeliki Lazaridou, Ngoc-Quan Pham, Raffaella Bernardi, Sandro Pezzelle, Marco Baroni, Gemma Boleda, and Raquel Fernández. 2016 · 2016
Earlier work this paper cites.
What you can cram into a single vector: Probing sentence embeddings for linguistic properties
Alexis Conneau, Germán Kruszewski, Guillaume Lample, Loïc Barrault, and Marco Baroni. 2018 · 2018
Earlier work this paper cites.
Breaking nli systems with sentences that require simple lexical inferences
Max Glockner, Vered Shwartz, and Yoav Goldberg. 2018 · 2018
Earlier work this paper cites.
Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A Smith. 2018 · 2018
Earlier work this paper cites.
Stress test evaluation for natural language inference
Aakanksha Naik, Abhilasha Ravichander, Norman Sadeh, Carolyn Rose, and Graham Neubig. 2018 · 2018
Earlier work this paper cites.
Hypothesis only baselines in natural language inference
Adam Poliak, Jason Naradowsky, Aparajita Haldar, Rachel Rudinger, and Benjamin Van Durme. 2018 · 2018
Earlier work this paper cites.
Lessons from natural language inference in the clinical domain
Alexey Romanov and Chaitanya Shivade. 2018 · 2018
Earlier work this paper cites.
Glue: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2018 · 2018
Earlier work this paper cites.
Overview of the MEDIQA 2019 shared task on textual inference, question entailment and question answering
Asma Ben Abacha, Chaitanya Shivade, and Dina Demner-Fushman. 2019 · 2019
Earlier work this paper cites.
Publicly available clinical bert embeddings
Emily Alsentzer, John Murphy, William Boag, Wei-Hung Weng, Di Jindi, Tristan Naumann, and Matthew McDermott. 2019 · 2019
Cited alongside, same era.
SciBERT: A pretrained language model for scientific text
Iz Beltagy, Kyle Lo, and Arman Cohan. 2019 · 2019
Cited alongside, same era.
COMET: commonsense transformers for automatic knowledge graph construction
Antoine Bosselut, Hannah Rashkin, Maarten Sap, Chaitanya Malaviya, Asli Çelikyilmaz, and Yejin Choi. 2019 · 2019
Cited alongside, same era.
Commonsense knowledge mining from pretrained models
Joe Davison, Joshua Feldman, and Alexander M. Rush. 2019 · 2019
Cited alongside, same era.
BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Do neural language representations learn physical commonsense?
Superglue: A stickier benchmark for general-purpose language understanding systems
Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman. 2019 · 2019
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WTMED at MEDIQA 2019: A hybrid approach to biomedical natural language inference
Zhaofeng Wu, Yan Song, Sicong Huang, Yuanhe Tian, and Fei Xia. 2019 · 2019
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Stress test evaluation of transformer-based models in natural language understanding tasks
Carlos Aspillaga, Andrés Carvallo, and Vladimir Araujo. 2020 · 2020
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Clinical concept embeddings learned from massive sources of multimodal medical data
Andrew L Beam, Benjamin Kompa, Allen Schmaltz, Inbar Fried, Griffin Weber, Nathan Palmer, Xu Shi, Tianxi Cai, and Isaac S Kohane. 2020 · 2020
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Inducing relational knowledge from BERT
Zied Bouraoui, José Camacho-Collados, and Steven Schockaert. 2020 · 2020
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Maxwell Forbes, Ari Holtzman, and Yejin Choi. 2019 · 2019
Cited alongside, same era.
PubMedQA: A dataset for biomedical research question answering
Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William Cohen, and Xinghua Lu. 2019b · 2019
Cited alongside, same era.
UW-BHI at MEDIQA 2019: An analysis of representation methods for medical natural language inference
William Kearns, Wilson Lau, and Jason Thomas. 2019 · 2019
Cited alongside, same era.
BioBERT: a 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 · 2019
Cited alongside, same era.
Incorporating domain knowledge into natural language inference on clinical texts
Mingming Lu, Yu Fang, Fengqi Yan, and Maozhen Li. 2019 · 2019
Cited alongside, same era.
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 · 2019
Cited alongside, same era.
Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick S. H. Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander H. Miller. 2019 · 2019
Cited alongside, same era.
Enhancing clinical bert embedding using a biomedical knowledge base
Boran Hao, Henghui Zhu, and Ioannis Paschalidis. 2020 · 2020
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Infusing disease knowledge into BERT for health question answering, medical inference and disease name recognition
Yun He, Ziwei Zhu, Yin Zhang, Qin Chen, and James Caverlee. 2020 · 2020
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How can we know what language models know
Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham Neubig. 2020 · 2020
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BioBERT: a 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. 2020 · 2020
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Pretrained language models for biomedical and clinical tasks: Understanding and extending the state-of-the-art
Patrick Lewis, Myle Ott, Jingfei Du, and Veselin Stoyanov. 2020 · 2020
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Adversarial NLI: A new benchmark for natural language understanding
Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela. 2020 · 2020
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How much knowledge can you pack into the parameters of a language model?
Adam Roberts, Colin Raffel, and Noam Shazeer. 2020 · 2020
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oLMpics - on what language model pre-training captures
Alon Talmor, Yanai Elazar, Yoav Goldberg, and Jonathan Berant. 2020 · 2020
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