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Information extraction tasks such as event extraction require an in-depth understanding of the output structure and sub-task dependencies.
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y.; Ott, M.; Goyal, N.; Du, J.; Joshi, M.; Chen, D.; Levy, O.; Lewis, M.; Zettlemoyer, L.; and Stoyanov, V. 2019 · 1907
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The Automatic Content Extraction (ACE) Program – Tasks, Data, and Evaluation
Doddington, G.; Mitchell, A.; Przybocki, M.; Ramshaw, L.; Strassel, S.; and Weischedel, R. 2004 · 2004
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Position-Aware Attention and Supervised Data Improve Slot Filling
Zhang, Y.; Zhong, V.; Chen, D.; Angeli, G.; and Manning, C. D. 2017 · 2017
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Zero-Shot Transfer Learning for Event Extraction
Huang, L.; Ji, H.; Cho, K.; Dagan, I.; Riedel, S.; and Voss, C. 2018 · 2018
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A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference
Williams, A.; Nangia, N.; and Bowman, S. 2018 · 2018
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Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods
Zhao, J.; Wang, T.; Yatskar, M.; Ordonez, V.; and Chang, K.-W. 2018 · 2018
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Data Augmentation Using Pre-trained Transformer Models
Kumar, V.; Choudhary, A.; and Cho, E. 2020 · 2020
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BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
Lewis, M.; Liu, Y.; Goyal, N.; Ghazvininejad, M.; Mohamed, A.; Levy, O.; Stoyanov, V.; and Zettlemoyer, L. 2020 · 2020
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A Joint Neural Model for Information Extraction with Global Features
Lin, Y.; Ji, H.; Huang, F.; and Wu, L. 2020 · 2020
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LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
Yamada, I.; Asai, A.; Shindo, H.; Takeda, H.; and Matsumoto, Y. 2020 · 2020
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Pegasus: Pre-training with extracted gap-sentences for abstractive summarization
Zhang, J.; Zhao, Y.; Saleh, M.; and Liu, P. 2020 · 2020
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Making Pre-trained Language Models Better Few-shot Learners
Gao, T.; Fisch, A.; and Chen, D. 2021 · 2021
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Generate, Annotate, and Learn: Generative Models Advance Self-Training and Knowledge Distillation
He, X.; Nassar, I.; Kiros, J. R.; Haffari, G.; and Norouzi, M. 2021 · 2021
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Neural data augmentation via example extrapolation
Lee, K.; Guu, K.; He, L.; Dozat, T.; and Chung, H. W. 2021 · 2021
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Document-Level Event Argument Extraction by Conditional Generation
Li, S.; Ji, H.; and Han, J. 2021 · 2021
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Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction
Lu, Y.; Lin, H.; Xu, J.; Han, X.; Tang, J.; Li, A.; Sun, L.; Liao, M.; and Chen, S. 2021 · 2021
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Zero-Shot Event Extraction via Transfer Learning: Challenges and Insights
Lyu, Q.; Zhang, H.; Sulem, E.; and Roth, D. 2021 · 2021
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HyperExpan: Taxonomy Expansion with Hyperbolic Representation Learning
Ma, M. D.; Chen, M.; Wu, T.-L.; and Peng, N. 2021a · 2021
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EventPlus: A Temporal Event Understanding Pipeline
Ma, M. D.; Sun, J.; Yang, M.; Huang, K.-H.; Wen, N.; Singh, S.; Han, R.; and Peng, N. 2021b · 2021
Cited alongside, same era.
Generating Datasets with Pretrained Language Models
Schick, T.; and Schütze, H. 2021 · 2021
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Zero-Shot Label-Aware Event Trigger and Argument Classification
Zhang, H.; Wang, H.; and Roth, D. 2021 · 2021
Cited alongside, same era.
On the Intrinsic and Extrinsic Fairness Evaluation Metrics for Contextualized Language Representations
Cao, Y.; Pruksachatkun, Y.; Chang, K.-W.; Gupta, R.; Kumar, V.; Dhamala, J.; and Galstyan, A. 2022 · 2022
Zhou, Y.; Kaneko, M.; and Bollegala, D. 2022 · 2022
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Exploring the Feasibility of ChatGPT for Event Extraction
Gao, J.; Zhao, H.; Yu, C.; and Xu, R. 2023 · 2023
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Han, R.; Peng, T.; Yang, C.; Wang, B.; Liu, L.; and Wan, X. 2023 · 2023
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Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extraction
Josifoski, M.; Sakota, M.; Peyrard, M.; and West, R. 2023 · 2023
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RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction
Chia, Y. K.; Bing, L.; Poria, S.; and Si, L. 2022 · 2022
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Generative Prompt Tuning for Relation Classification
Han, J.; Zhao, S.; Cheng, B.; Ma, S.; and Lu, W. 2022 · 2022
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DEGREE: A Data-Efficient Generation-Based Event Extraction Model
Hsu, I.-H.; Huang, K.-H.; Boschee, E.; Miller, S.; Natarajan, P.; Chang, K.-W.; and Peng, N. 2022 · 2022
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Summarization as Indirect Supervision for Relation Extraction
Lu, K.; Hsu, I.-H.; Zhou, W.; Ma, M. D.; and Chen, M. 2022 · 2022
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Generating Training Data with Language Models: Towards Zero-Shot Language Understanding
Meng, Y.; Huang, J.; Zhang, Y.; and Han, J. 2022 · 2022
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Chatgpt: Optimizing language models for dialogue
OpenAI. 2022 · 2022
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Li, B.; Fang, G.; Yang, Y.; Wang, Q.; Ye, W.; Zhao, W.; and Zhang, S. 2023 · 2023
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Chain of Hindsight Aligns Language Models with Feedback
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Multi-hop Evidence Retrieval for Cross-document Relation Extraction
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Parameter-Efficient Low-Resource Dialogue State Tracking by Prompt Tuning
Ma, M. D.; Kao, J.-Y.; Gao, S.; Gupta, A.; Jin, D.; Chung, T.; and Peng, N. 2023a · 2023
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Self-Refine: Iterative Refinement with Self-Feedback
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Does Synthetic Data Generation of LLMs Help Clinical Text Mining?
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Code4Struct: Code Generation for Few-Shot Event Structure Prediction
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Self-Instruct: Aligning Language Models with Self-Generated Instructions
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Can NLI Provide Proper Indirect Supervision for Low-resource Biomedical Relation Extraction?
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Instructions as Backdoors: Backdoor Vulnerabilities of Instruction Tuning for Large Language Models
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