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Several recent efforts have been devoted to enhancing pre-trained language models (PLMs) by utilizing extra heterogeneous knowledge in knowledge graphs (KGs) and achieved consistent improvements on various knowledge-driven NLP tasks.
Tagme: On-the-fly annotation of short text fragments (by wikipedia entities)
Paolo Ferragina and Ugo Scaiella. 2010 · 2010
Earlier work this paper cites.
Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013 · 2013
Earlier work this paper cites.
Design challenges for entity linking
Xiao Ling, Sameer Singh, and Daniel S. Weld. 2015 · 2015
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Position-aware attention and supervised data improve slot filling
Yuhao Zhang, Victor Zhong, Danqi Chen, Gabor Angeli, and Christopher D. Manning. 2017 · 2017
Earlier work this paper cites.
Ultra-fine entity typing
Eunsol Choi, Omer Levy, Yejin Choi, and Luke Zettlemoyer. 2018 · 2018
Earlier work this paper cites.
FewRel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation
Xu Han, Hao Zhu, Pengfei Yu, Ziyun Wang, Yuan Yao, Zhiyuan Liu, and Maosong Sun. 2018 · 2018
Earlier work this paper cites.
Deep contextualized word representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Earlier work this paper cites.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
Earlier work this paper cites.
Matching the blanks: Distributional similarity for relation learning
Livio Baldini Soares, Nicholas FitzGerald, Jeffrey Ling, and Tom Kwiatkowski. 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.
Integrating graph contextualized knowledge into pre-trained language models
Bin He, Di Zhou, Jinghui Xiao, Xin jiang, Qun Liu, Nicholas Jing Yuan, and Tong Xu. 2019 · 2019
Cited alongside, same era.
Specializing unsupervised pretraining models for word-level semantic similarity
Anne Lauscher, Ivan Vulić, Edoardo Maria Ponti, Anna Korhonen, and Goran Glavaš. 2019 · 2019
Cited alongside, same era.
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 · 2019
Cited alongside, same era.
Pretrained encyclopedia: Weakly supervised knowledge-pretrained language model
Wenhan Xiong, Jingfei Du, William Yang Wang, and Veselin Stoyanov. 2019 · 2019
Later among the works it cites.
Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019 · 2019
Later among the works it cites.
Sensebert: Driving some sense into bert
Levine Yoav, Lenz Barak, Dagan Or, Padnos Dan, Sharir Or, Shalev-Shwartz Shai, Shashua Amnon, and Shoham Yoav. 2019 · 2019
Later among the works it cites.
Ernie: Enhanced representation through knowledge integration
Sun Yu, Wang Shuohuan, Li Yu-Kun, Feng Shikun, Chen Xuyi, Zhang Han, Tian Xin, Zhu Danxiang, Tian Hao, and Wu Hua. 2019 · 2019
Later among the works it cites.
Ernie: Enhanced language representation with informative entities
Zhengyan Zhang, Xu Han, Zhiyuan Liu, Xin Jiang, Maosong Sun, and Qun Liu. 2019 · 2019
Later among the works it cites.
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Spanbert: Improving pre-training by representing and predicting spans
Joshi Mandar, Chen Danqi, Liu Yinhan, Daniel S. Weld, Zettlemoyer Luke, and Levy Omer. 2019 · 2019
Cited alongside, same era.
Knowledge enhanced contextual word representations
Matthew E. Peters, Mark Neumann, Robert L Logan, Roy Schwartz, Vidur Joshi, Sameer Singh, and Noah A. Smith. 2019 · 2019
Cited alongside, same era.
BERT is not a knowledge base (yet): Factual knowledge vs. name-based reasoning in unsupervised QA
Nina Poerner, Ulli Waltinger, and Hinrich Schütze. 2019 · 2019
Cited alongside, same era.
KEPLER: A unified model for knowledge embedding and pre-trained language representation
Xiaozhi Wang, Tianyu Gao, Zhaocheng Zhu, Zhiyuan Liu, Juan-Zi Li, and Jian Tang. 2019 · 2019
Cited alongside, same era.
Weijie Liu, Peng Zhou, Zhe Zhao, Zhiruo Wang, Qi Ju, Haotang Deng, and Ping Wang. 2020 · 2020
Closest in time.
K-adapter: Infusing knowledge into pre-trained models with adapters
Ruize Wang, Duyu Tang, Nan Duan, Zhongyu Wei, Xuanjing Huang, Jianshu Ji, Cuihong Cao, Daxin Jiang, and Ming Zhou. 2020 · 2020
Closest in time.
Ernie2.0: A continual pre-training framework for language understanding
Sun Yu, Wang Shuohuan, Li Yukun, Feng Shikun, Tian Hao, Wu Hua, and Wang Haifeng. 2020 · 2020
Closest in time.