| Challenge: | In this paper, we study two types of relation between events in text documents. |
| Approach: | They propose a graph-based decoding algorithm that is applicable to both tasks . they propose ES and EH to solve the event coreference problem . |
| Outcome: | The proposed decoding algorithm beats a strong temporal-based, oracle-informed baseline. |
Similar Papers
Enhancing Unrestricted Cross-Document Event Coreference with Graph Reconstruction Networks (2024.lrec-main)
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| Challenge: | Event Coreference Resolution is a discourse-oriented task that requires a lot of computational power. |
| Approach: | They propose a method to combine traditional mention-pair coreference models with a graph reconstruction algorithm. |
| Outcome: | The proposed method is highly robust in low-data settings and scales with increases in performance for the underlying mention-pair models. |
The Coreference under Transformation Labeling Dataset: Entity Tracking in Procedural Texts Using Event Models (2023.findings-acl)
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| Challenge: | et al., 2023) show that entity coreference resolution is improved when events bring about changes in entities that are not reflected in text mentions. |
| Approach: | They propose to perform transformation-based entity linking prior to coreference relation identification to improve entity coreference. |
| Outcome: | The proposed model improves coreference resolution of entities mentioned under a process-oriented model of events. |
Improving Event Coreference Resolution by Modeling Correlations between Event Coreference Chains and Document Topic Structures (P18-1)
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| Challenge: | a novel approach for event coreference resolution models correlations between event chains and document topical structures. |
| Approach: | They propose a novel approach that models correlations between event coreference chains and document topical structures through an Integer Linear Programming formulation. |
| Outcome: | The proposed approach improves performance across a dataset of document topics . it shows that the models can identify and link event mentions that refer to the same event . |
Enhancing Cross-Document Event Coreference Resolution by Discourse Structure and Semantic Information (2024.lrec-main)
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| Challenge: | Existing cross-document event coreference resolution models lack the ability to capture long-distance dependencies. |
| Approach: | They propose to construct document-level Rhetorical Structure Theory trees and cross-document Lexical Chains to model structural and semantic information of documents. |
| Outcome: | The proposed model outperforms baseline models on English and Chinese datasets by large margins. |
Learning Event-aware Measures for Event Coreference Resolution (2023.findings-acl)
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| Challenge: | Existing models for event coreference resolution are based on entity-level tasks, but event coreferent resolution is a challenge. |
| Approach: | They propose a model that learns and integrates multiple representations from event alone and event pair on the basis of event but not entity as before. |
| Outcome: | The proposed model achieves new state-of-the-art on the ACE 2005 benchmark, demonstrating the effectiveness of the proposed framework. |
Graph Refinement for Coreference Resolution (2022.findings-acl)
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| Challenge: | Existing models for coreference resolution are based on independent mention pair-wise decisions. |
| Approach: | They propose a model that learns coreference at the document-level and takes global decisions. |
| Outcome: | The proposed model improves over baselines, reinforcing the hypothesis that document-level information improves conference resolution. |
Text-to-Text Extraction and Verbalization of Biomedical Event Graphs (2022.coling-1)
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| Challenge: | Biomedical events represent complex, graphical, and semantically rich interactions expressed in the scientific literature. |
| Approach: | They propose a framework to solve event extraction and event verbalization with a unified text-to-text approach. |
| Outcome: | The proposed framework achieves greater state-of-the-art performance than single-task competitors and can generate coherent natural language utterances from structured data. |
Event Detection as Graph Parsing (2021.findings-acl)
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| Challenge: | Existing approaches to event detection focus on using syntactic dependency structures or external knowledge to boost the performance. |
| Approach: | They propose a graph parsing problem that explicitly models multiple event correlations and utilizes rich information conveyed by event type and subtype. |
| Outcome: | The proposed model outperforms existing models on the public ACE2005 dataset by 4.2% on the dataset. |
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. |
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. |
| Approach: | They propose a neural architecture for cross-document coreference resolution by representing an event mention using its lexical span, surrounding context, and relation to other mentions via predicate-arguments structures. |
| Outcome: | The proposed model outperforms the state-of-the-art event coreference model on ECB+ while providing the first entity coreference results on this corpus. |