Dynamic Prefix-Tuning for Generative Template-based Event Extraction (2022.acl-long)
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| Challenge: | Experimental results show that our model achieves competitive results with the state-of-the-art classification-based model OneIE on ACE 2005. |
| Approach: | They propose a generative template-based event extraction method with dynamic prefix . they integrate context information with type-specific prefixes to learn a context-specific name for each context . |
| Outcome: | The proposed method achieves competitive results with state-of-the-art model OneIE on ACE 2005 and performs well on ERE. |
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| Challenge: | Recent advances in event argument extraction (EAE) involve incorporating useful auxiliary information into models during training and inference. |
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| Challenge: | Experimental results show that our method outperforms all strong baselines and can be generalized to various datasets. |
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| Challenge: | Existing generation-based EAE models focus on problem re-formulation and prompt design without incorporating additional information that has been shown to be effective for classification-based models. |
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| Challenge: | Existing methods to extract arguments from documents are based on generating and post-processing a complex target sequence (template). |
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| Challenge: | Recent work on Event Extraction addresses the error propagation issue found in token-based classification approaches. |
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I-Hung Hsu, Kuan-Hao Huang, Elizabeth Boschee, Scott Miller, Prem Natarajan, Kai-Wei Chang, Nanyun Peng
| Challenge: | Existing models for event extraction require expensive human annotations. |
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| Challenge: | Experimental results show that a well-defined and comprehensive description of event types can significantly improve event detection performance when the annotations are limited. |
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| Challenge: | a novel system allows users to customize event extraction to find new event types and their arguments. |
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Prompt for Extraction? PAIE: Prompting Argument Interaction for Event Argument Extraction (2022.acl-long)
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| Challenge: | Using a prompt-based model, we find that event argument extraction is efficient and generalized well to few-shot settings. |
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Document-Level Event Argument Extraction by Conditional Generation (2021.naacl-main)
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| Challenge: | Existing event extraction models have been limited to the sentence level . this formulation signifies a misalignment between the information seeking behavior and the informative seeking behavior. |
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