| Challenge: | Recent event extraction methods rely on pre-trained language models but still suffer from errors due to a lack of syntactic knowledge. |
| Approach: | They propose a method to incorporate syntactic information into PLM-based models for event extraction (EE) this method uses a standard dependency corpus to select syntax-related dimensions of the model's representation. |
| Outcome: | The proposed method outperforms baseline models and existing syntactic reinforcement methods on sentence-level and document-level EE benchmark datasets. |
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| Challenge: | Event extraction (EE) is a critical task in natural language processing, yet deploying a practical EE system remains challenging. |
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| Challenge: | lexicosyntactic inferences are triggered by surprising aspects of the syntactical context that a word occurs in. |
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Zenan Xu, Daya Guo, Duyu Tang, Qinliang Su, Linjun Shou, Ming Gong, Wanjun Zhong, Xiaojun Quan, Daxin Jiang, Nan Duan
| Challenge: | Existing methods that use syntax of text in pre-training and fine-tuning suffer from discrepancy between the two stages. |
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| Challenge: | Currently, most neural machine translation models rely on pairs of parallel sentences, assuming syntactic information is automatically learned by an attention mechanism. |
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CLEVE: Contrastive Pre-training for Event Extraction (2021.acl-long)
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Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach (2020.acl-main)
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| Challenge: | incorporating syntactic structure into language models has been a challenge since the 1990s. |
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