CorefPrompt: Prompt-based Event Coreference Resolution by Measuring Event Type and Argument Compatibilities (2023.emnlp-main)
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| Challenge: | Existing methods for event coreference resolution (ECR) do not leverage human-summarized rules to guide the model. |
| Approach: | They propose to transform ECR into a cloze-style MLM task using a prompt-based approach . they introduce two auxiliary prompt tasks, event-type compatibility and argument compatibility . |
| Outcome: | The proposed method performs well in a state-of-the-art (SOTA) benchmark. |
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| Challenge: | Existing methods for training coreference systems sample from a largely skewed distribution, making it difficult to learn coreference beyond surface matching. |
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Cross-Document Event Coreference Resolution on Discourse Structure (2023.emnlp-main)
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| Challenge: | Experimental results show that our proposed model outperforms several baselines and achieves the competitive performance with the start-of-the-art baselines. |
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Improving Event Coreference Resolution Using Document-level and Topic-level Information (2022.emnlp-main)
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| Challenge: | Experimental results show that our model outperforms the SOTA baselines due to the encoding length limitation. |
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Linear Cross-document Event Coreference Resolution with X-AMR (2024.lrec-main)
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Shafiuddin Rehan Ahmed, George Arthur Baker, Evi Judge, Michael Reagan, Kristin Wright-Bettner, Martha Palmer, James H. Martin
| Challenge: | Event Coreference Resolution (ECR) is expensive both for automated systems and manual annotations. |
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Event Coreference Resolution with their Paraphrases and Argument-aware Embeddings (2020.coling-main)
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| Challenge: | Existing methods for event coreference resolution do not identify paraphrase relations between events. |
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Synergetic Event Understanding: A Collaborative Approach to Cross-Document Event Coreference Resolution with Large Language Models (2024.acl-long)
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| Challenge: | Existing approaches to cross-document event coreference resolution are prone to learning simple co-occurrences due to the complexity of contexts. |
| Approach: | They propose a collaborative approach to cross-document event coreference resolution that leverages both a universally capable LLM and a task-specific SLM. |
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Event Coreference Resolution with Non-Local Information (2020.aacl-main)
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| Challenge: | Existing joint models for event coreference resolution are understudied and underexploited . current models only learn trigger detection and event coreference from annotated training data . |
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A Rationale-centric Counterfactual Data Augmentation Method for Cross-Document Event Coreference Resolution (2024.naacl-long)
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| Challenge: | Existing state-of-the-art event coreference resolution systems rely on spurious and spurious associations in the input mention pair text. |
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Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution (P19-1)
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| Challenge: | Recognizing that various textual spans across multiple texts refer to the same entity or event is an important NLP task. |
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Multimodal Cross-Document Event Coreference Resolution Using Linear Semantic Transfer and Mixed-Modality Ensembles (2024.lrec-main)
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Abhijnan Nath, Huma Jamil, Shafiuddin Rehan Ahmed, George Arthur Baker, Rahul Ghosh, James H. Martin, Nathaniel Blanchard, Nikhil Krishnaswamy
| Challenge: | Existing methods for cross-document coreference resolution do not provide images for all mentions of events. |
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