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Infusing factual knowledge into pre-trained models is fundamental for many knowledge-intensive tasks.
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 · 1904
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A fast and high quality multilevel scheme for partitioning irregular graphs
George Karypis and Vipin Kumar. 1998 · 1998
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K-adapter: Infusing knowledge into pre-trained models with adapters
Ruize Wang, Duyu Tang, Nan Duan, Zhongyu Wei, Xuanjing Huang, Cuihong Cao, Daxin Jiang, Ming Zhou, et al. 2020 · 2002
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The unified medical language system (umls): integrating biomedical terminology
Olivier Bodenreider. 2004 · 2004
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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. 2020 · 2007
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Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang, and Peter Szolovits. 2020 · 2009
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Jaket: Joint pre-training of knowledge graph and language understanding
Donghan Yu, Chenguang Zhu, Yiming Yang, and Michael Zeng. 2020 · 2010
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DistDGL: Distributed graph neural network training for billion-scale graphs
Da Zheng, Chao Ma, Minjie Wang, Jinjing Zhou, Qidong Su, Xiang Song, Quan Gan, Zheng Zhang, and George Karypis. 2020 · 2010
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013 · 2013
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015 · 2015
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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016 · 2016
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Initializing neural networks for hierarchical multi-label text classification
Simon Baker and Anna Korhonen. 2017 · 2017
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole. 2017 · 2017
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Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean. 2017 · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2018 · 2018
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Lessons from natural language inference in the clinical domain
Alexey Romanov and Chaitanya Shivade. 2018 · 2018
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SciBERT: A pretrained language model for scientific text
Iz Beltagy, Kyle Lo, and Arman Cohan. 2019 · 2019
Ernie: Enhanced language representation with informative entities
Zhengyan Zhang, Xu Han, Zhiyuan Liu, Xin Jiang, Maosong Sun, and Qun Liu. 2019 · 2019
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Enhancing question answering by injecting ontological knowledge through regularization
Travis R Goodwin and Dina Demner-Fushman. 2020 · 2020
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Yun He, Ziwei Zhu, Yin Zhang, Qin Chen, and James Caverlee. 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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Towards medical machine reading comprehension with structural knowledge and plain text
Dongfang Li, Baotian Hu, Qingcai Chen, Weihua Peng, and Anqi Wang. 2020 · 2020
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Cited alongside, same era.
Cluster-GCN: An efficient algorithm for training deep and large graph convolutional networks
Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh. 2019 · 2019
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Parameter-efficient transfer learning for nlp
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019 · 2019
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PubMedQA: A dataset for biomedical research question answering
Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William Cohen, and Xinghua Lu. 2019 · 2019
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Results of the seventh edition of the BioASQ challenge
Anastasios Nentidis, Konstantinos Bougiatiotis, Anastasia Krithara, and Georgios Paliouras. 2019 · 2019
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Yifan Peng, Shankai Yan, and Zhiyong Lu. 2019 · 2019
Cited alongside, same era.
Knowledge enhanced contextual word representations
Matthew E Peters, Mark Neumann, Robert Logan, Roy Schwartz, Vidur Joshi, Sameer Singh, and Noah A Smith. 2019 · 2019
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Pretrained encyclopedia: Weakly supervised knowledge-pretrained language model
Wenhan Xiong, Jingfei Du, William Yang Wang, and Veselin Stoyanov. 2019 · 2019
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Later among the works it cites.
Overview of BioASQ 2020: The eighth BioASQ challenge on large-scale biomedical semantic indexing and question answering
Anastasios Nentidis, Anastasia Krithara, Konstantinos Bougiatiotis, Martin Krallinger, Carlos Rodriguez-Penagos, Marta Villegas, and Georgios Paliouras. 2020 · 2020
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Colake: Contextualized language and knowledge embedding
Tianxiang Sun, Yunfan Shao, Xipeng Qiu, Qipeng Guo, Yaru Hu, Xuan-Jing Huang, and Zheng Zhang. 2020 · 2020
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Autoregressive entity retrieval
Nicola De Cao, Gautier Izacard, Sebastian Riedel, and Fabio Petroni. 2021 · 2021
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Self-alignment pre-training for biomedical entity representations
Fangyu Liu, Ehsan Shareghi, Zaiqiao Meng, Marco Basaldella, and Nigel Collier. 2021 · 2021
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KILT: a benchmark for knowledge intensive language tasks
Fabio Petroni, Aleksandra Piktus, Angela Fan, Patrick Lewis, Majid Yazdani, Nicola De Cao, James Thorne, Yacine Jernite, Vladimir Karpukhin, Jean Maillard, et al. 2021 · 2021
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KEPLER: A unified model for knowledge embedding and pre-trained language representation
Xiaozhi Wang, Tianyu Gao, Zhaocheng Zhu, Zhengyan Zhang, Zhiyuan Liu, Juanzi Li, and Jian Tang. 2021 · 2021
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Improving biomedical pretrained language models with knowledge
Zheng Yuan, Yijia Liu, Chuanqi Tan, Songfang Huang, and Fei Huang. 2021 · 2021
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