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Pre-trained language representation models (PLMs) cannot well capture factual knowledge from text.
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. 2019c · 1907
Earlier work this paper cites.
Open-World Knowledge Graph Completion
Baoxu Shi and Tim Weninger. 2018 · 1964
Earlier work this paper cites.
Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen. 1989 · 1989
Earlier work this paper cites.
WordNet: A Lexical Database for English
George A. Miller. 1995 · 1995
Earlier work this paper cites.
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 · 2002
Earlier work this paper cites.
A unified architecture for natural language processing: Deep neural networks with multitask learning
Ronan Collobert and Jason Weston. 2008 · 2008
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Translating Embeddings for Modeling Multi-relational Data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013 · 2013
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Distributed Representations of Words and Phrases and their Compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Gregory S. Corrado, and Jeffrey Dean. 2013 · 2013
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GloVe: Global Vectors for Word Representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
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Knowledge Graph and Text Jointly Embedding
Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. 2014 · 2014
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Semi-supervised sequence learning
Andrew M Dai and Quoc V Le. 2015 · 2015
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Learning Entity and Relation Embeddings for Knowledge Graph Completion
Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015 · 2015
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Embedding Entities and Relations for Learning and Inference in Knowledge Bases
Bishan Yang, Scott Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. 2015 · 2015
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Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books
Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015 · 2015
Earlier work this paper cites.
Neural Machine Translation of Rare Words with Subword Units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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Representation Learning of Knowledge Graphs with Entity Descriptions
Ruobing Xie, Zhiyuan Liu, Jia Jia, Huanbo Luan, and Maosong Sun. 2016 · 2016
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Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation
Ikuya Yamada, Hiroyuki Shindo, Hideaki Takeda, and Yoshiyasu Takefuji. 2016 · 2016
Earlier work this paper cites.
Bridge Text and Knowledge by Learning Multi-Prototype Entity Mention Embedding
Yixin Cao, Lifu Huang, Heng Ji, Xu Chen, and Juanzi Li. 2017 · 2017
Earlier work this paper cites.
Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approach
Takuo Hamaguchi, Hidekazu Oiwa, Masashi Shimbo, and Yuji Matsumoto. 2017 · 2017
Earlier work this paper cites.
Inductive Representation Learning on Large Graphs
William L. Hamilton, Rex Ying, and Jure Leskovec. 2017 · 2017
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Prototypical Networks for Few-shot Learning
Jake Snell, Kevin Swersky, and Richard Zemel. 2017 · 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 · 2017
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Leveraging Knowledge Bases in LSTMs for Improving Machine Reading
Bishan Yang and Tom Mitchell. 2017 · 2017
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Position-aware Attention and Supervised Data Improve Slot Filling
Yuhao Zhang, Victor Zhong, Danqi Chen, Gabor Angeli, and Christopher D. Manning. 2017 · 2017
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Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking
Aleksandar Bojchevski and Stephan Günnemann. 2018 · 2018
Barack’s Wife Hillary: Using Knowledge Graphs for Fact-Aware Language Modeling
Robert Logan, Nelson F. Liu, Matthew E. Peters, Matt Gardner, and Sameer Singh. 2019 · 2019
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Zero-Shot Entity Linking by Reading Entity Descriptions
Lajanugen Logeswaran, Ming-Wei Chang, Kenton Lee, Kristina Toutanova, Jacob Devlin, and Honglak Lee. 2019 · 2019
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fairseq: A Fast, Extensible Toolkit for Sequence Modeling
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019 · 2019
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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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Language Models as Knowledge Bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller. 2019 · 2019
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RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
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Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision
Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu, Chengjiang Li, Xu Chen, and Tiansi Dong. 2018 · 2018
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Ultra-Fine Entity Typing
Eunsol Choi, Omer Levy, Yejin Choi, and Luke Zettlemoyer. 2018 · 2018
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Universal Language Model Fine-tuning for Text Classification
Jeremy Howard and Sebastian Ruder. 2018 · 2018
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SimplE Embedding for Link Prediction in Knowledge Graphs
Seyed Mehran Kazemi and David Poole. 2018 · 2018
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Deep Contextualized Word Representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Improving Language Understanding by Generative Pre-Training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
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Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, and Jian Tang. 2019 · 2019
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Pretrained Encyclopedia: Weakly Supervised Knowledge-Pretrained Language Model
Wenhan Xiong, Jingfei Du, William Yang Wang, and Stoyanov Veselin. 2019 · 2019
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XLNet: Generalized Autoregressive Pretraining for Language Understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime G. Carbonell, Ruslan Salakhutdinov, and Quoc V. Le. 2019 · 2019
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Zhengyan Zhang, Xu Han, Zhiyuan Liu, Xin Jiang, Maosong Sun, and Qun Liu. 2019 · 2019
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GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding
Zhaocheng Zhu, Shizhen Xu, Jian Tang, and Meng Qu. 2019 · 2019
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Latent Relation Language Models
Hiroaki Hayashi, Zecong Hu, Chenyan Xiong, and Graham Neubig. 2020 · 2020
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Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly
Nora Kassner and Hinrich Schütze. 2020 · 2020
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ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
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K-BERT: Enabling Language Representation with Knowledge Graph
Weijie Liu, Peng Zhou, Zhe Zhao, Zhiruo Wang, Qi Ju, Haotang Deng, and Ping Wang. 2020 · 2020
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E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT
Nina Poerner, Ulli Waltinger, and Hinrich Schütze. 2020 · 2020
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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. 2020 · 2020
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Scalable Zero-shot Entity Linking with Dense Entity Retrieval
Ledell Wu, Fabio Petroni, Martin Josifoski, Sebastian Riedel, and Luke Zettlemoyer. 2020 · 2020
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Complex Embeddings for Simple Link Prediction
Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard. 2016 · 2080
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