Papers with ECR
Which Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluation (2024.findings-acl)
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Hao Li, Yuping Wu, Viktor Schlegel, Riza Batista-Navarro, Tharindu Madusanka, Iqra Zahid, Jiayan Zeng, Xiaochi Wang, Xinran He, Yizhi Li, Goran Nenadic
| Challenge: | Recent advances in large language models (LLMs) have made it difficult to build an automated debate system that helps people to synthesise persuasive arguments. |
| Approach: | They propose to use an argument mining dataset to capture the end-to-end process of preparing an argumentative essay for a debate. |
| Outcome: | The proposed dataset shows that it performs better on individual tasks than on human-centred evaluations. |
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. |
| Approach: | They propose a rationale-centric counterfactual data augmentation method that leverages the debiasing capability of counterfact data haussed by LLM-in-the-loop to mitigate spurious association while emphasizing causation. |
| Outcome: | The proposed method achieves state-of-the-art on three popular cross-document benchmarks and demonstrates robustness in out-of domain scenarios. |
2*n is better than n2: Decomposing Event Coreference Resolution into Two Tractable Problems (2023.findings-acl)
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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. |
| Approach: | They propose a heuristic to efficiently filter out a large number of non-coreferent pairs and a training approach on a balanced set of coreferent and non- coreferente mention pairs. |
| Outcome: | The proposed approach significantly reduces compute requirements on two popular ECR datasets while reducing the computational complexity. |
Okay, Let’s Do This! Modeling Event Coreference with Generated Rationales and Knowledge Distillation (2024.naacl-long)
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| Challenge: | Recent work shows that generative large language models (LLMs) can be used to solve cross-document coreference problems. |
| Approach: | They propose rationale-oriented event clustering and knowledge distillation methods for event coreference scoring that leverage enriched information from the FTRs for improved CDCR. |
| Outcome: | The proposed model achieves SOTA B3 F1 on the ECB+ and GVC corpora without additional annotation or expensive document clustering. |
MCECR: A Novel Dataset for Multilingual Cross-Document Event Coreference Resolution (2024.findings-naacl)
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| Challenge: | Existing datasets for event coreference resolution focus on within-document event coreference and English text, lacking cross-document ECR datasets beyond English. |
| Approach: | They propose a multiligual dataset that manually annotates documents for event mentions and coreference in five languages. |
| Outcome: | The proposed dataset annotates documents for event mentions and coreference in five languages . the dataset fetches related news articles from the google search engine to increase the number of non-singleton clusters . |
Exploiting Document Structures and Cluster Consistencies for Event Coreference Resolution (2021.acl-long)
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| Challenge: | Existing deep learning models for event coreference resolution are limited in that they cannot exploit important interactions between relevant objects for ECR. |
| Approach: | They propose a deep learning model that groups coreferent event mentions into the same clusters . they use document structures to capture relevant objects for ECR . |
| Outcome: | The proposed model achieves state-of-the-art on two benchmark datasets. |
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. |
| Approach: | They propose a longformer-based encoder and an encoder with a trigger-mask mechanism to learn sentence-level embeddings based on local context. |
| Outcome: | The proposed model outperforms the baselines on the KBP 2017 dataset. |
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. |
| Approach: | They propose a graphical representation of events anchored around individual mentions using a cross-document version of Abstract Meaning Representation. |
| Outcome: | The proposed model is anchored around individual mentions using a cross-document version of Abstract Meaning Representation. |
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. |
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. |
| Approach: | They propose a multimodal cross-document event coreference resolution method that integrates visual and textual cues with a simple linear map between vision and language models. |
| Outcome: | The proposed method improves on a popular ECB+ and AIDA datasets. |