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Medical multiple-choice question answering (MCQA) is particularly difficult.
The umls metathesaurus: representing different views of biomedical concepts
P Schuyler, W Hole, Mark Tuttle, and David Sherertz. 1993 · 1993
Earlier work this paper cites.
Medical knowledge reengineering—converting major portions of the umls into a terminological knowledge base
Stefan Schulz and Udo Hahn. 2001 · 2001
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.
Ye Liu, Shaika Chowdhury, Chenwei Zhang, Cornelia Caragea, and Philip S Yu. 2020 · 2008
Earlier work this paper cites.
Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang, and Peter Szolovits. 2020 · 2009
Earlier work this paper cites.
The probabilistic relevance framework: Bm25 and beyond
Stephen Robertson, Hugo Zaragoza, et al. 2009 · 2009
Earlier work this paper cites.
Efficient natural language response suggestion for smart reply
Matthew Henderson, Rami Al-Rfou, Brian Strope, Yun-Hsuan Sung, László Lukács, Ruiqi Guo, Sanjiv Kumar, Balint Miklos, and Ray Kurzweil. 2017 · 2017
Earlier work this paper cites.
Automatic distractor suggestion for multiple-choice tests using concept embeddings and information retrieval
Le An Ha and Victoria Yaneva. 2018 · 2018
Earlier work this paper cites.
emrQA: A large corpus for question answering on electronic medical records
Anusri Pampari, Preethi Raghavan, Jennifer Liang, and Jian Peng. 2018 · 2018
Earlier work this paper cites.
Sentence encoders on stilts: Supplementary training on intermediate labeled-data tasks
Jason Phang, Thibault Févry, and Samuel R Bowman. 2018 · 2018
Earlier work this paper cites.
Knowledge abstraction matching for medical question answering
Jun Chen, Jingbo Zhou, Zhenhui Shi, Bin Fan, and Chengliang Luo. 2019 · 2019
Earlier work this paper cites.
PubMedQA: A dataset for biomedical research question answering
Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William Cohen, and Xinghua Lu. 2019 · 2019
Cited alongside, same era.
Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller. 2019 · 2019
Cited alongside, same era.
Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
Cited alongside, same era.
HEAD-QA: A healthcare dataset for complex reasoning
David Vilares and Carlos Gómez-Rodríguez. 2019 · 2019
Cited alongside, same era.
A BERT-based distractor generation scheme with multi-tasking and negative answer training strategies
Ho-Lam Chung, Ying-Hong Chan, and Yao-Chung Fan. 2020 · 2020
Cited alongside, same era.
Domain-specific language model pretraining for biomedical natural language processing
Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon. 2021 · 2021
Later among the works it cites.
Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2021 · 2021
Later among the works it cites.
BioELECTRA:pretrained biomedical text encoder using discriminators
Kamal raj Kanakarajan, Bhuvana Kundumani, and Malaikannan Sankarasubbu. 2021 · 2021
Later among the works it cites.
MLEC-QA: A Chinese Multi-Choice Biomedical Question Answering Dataset
Jing Li, Shangping Zhong, and Kaizhi Chen. 2021 · 2021
Later among the works it cites.
UmlsBERT: Clinical domain knowledge augmentation of contextual embeddings using the Unified Medical Language System Metathesaurus
George Michalopoulos, Yuanxin Wang, Hussam Kaka, Helen Chen, and Alexander Wong. 2021 · 2021
Later among the works it cites.
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Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Ming-Wei Chang. 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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Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 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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Anna Rogers, Matt Gardner, and Isabelle Augenstein. 2021 · 2021
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K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
Ruize Wang, Duyu Tang, Nan Duan, Zhongyu Wei, Xuanjing Huang, Jianshu Ji, Guihong Cao, Daxin Jiang, and Ming Zhou. 2021 · 2021
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Medical entity relation verification with large-scale machine reading comprehension
Yuan Xia, Chunyu Wang, Zhenhui Shi, Jingbo Zhou, Chao Lu, Haifeng Huang, and Hui Xiong. 2021 · 2021
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Medmcqa: A large-scale multi-subject multi-choice dataset for medical domain question answering
Ankit Pal, Logesh Kumar Umapathi, and Malaikannan Sankarasubbu. 2022 · 2022
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Linkbert: Pretraining language models with document links
Michihiro Yasunaga, Jure Leskovec, and Percy Liang. 2022 · 2022
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