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Relation extraction (RE) models have been challenged by their reliance on training data with expensive annotations.
An efficient implementation of trie structures
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ROUGE: A package for automatic evaluation of summaries
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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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Ultra-fine entity typing
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Extracting relational facts by an end-to-end neural model with copy mechanism
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Matching the blanks: Distributional similarity for relation learning
Livio Baldini Soares, Nicholas FitzGerald, Jeffrey Ling, and Tom Kwiatkowski. 2019 · 2019
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Incorporating structured commonsense knowledge in story completion
Jiaao Chen, Jianshu Chen, and Zhou Yu. 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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Enriching pre-trained language model with entity information for relation classification
Shanchan Wu and Yifan He. 2019 · 2019
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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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Tacred revisited: A thorough evaluation of the tacred relation extraction task
Christoph Alt, Aleksandra Gabryszak, and Leonhard Hennig. 2020 · 2020
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SpanBERT: Improving pre-training by representing and predicting spans
Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S. Weld, Luke Zettlemoyer, and Omer Levy. 2020 · 2020
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BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
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REBEL: Relation extraction by end-to-end language generation
Pere-Lluís Huguet Cabot and Roberto Navigli. 2021 · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
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Relation classification with entity type restriction
Shengfei Lyu and Huanhuan Chen. 2021 · 2021
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Looking beyond sentence-level natural language inference for question answering and text summarization
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Planning with learned entity prompts for abstractive summarization
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Label verbalization and entailment for effective zero and few-shot relation extraction
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Structured prediction as translation between augmented natural languages
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Learning from Context or Names? An Empirical Study on Neural Relation Extraction
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K-adapter: Infusing knowledge into pre-trained models with adapters
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A frustratingly easy approach for entity and relation extraction
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Document-level relation extraction with adaptive thresholding and localized context pooling
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KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction
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Ultra-fine entity typing with indirect supervision from natural language inference
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Generative biomedical entity linking via knowledge base-guided pre-training and synonyms-aware fine-tuning
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