| 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 . |
| Approach: | They propose to add a topic-based trigger detection module and a preprocessing module to improve event coreference. |
| Outcome: | The proposed model yields the best results on the KBP 2017 English and Chinese datasets. |
Similar Papers
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. |
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 . |
Constrained Multi-Task Learning for Event Coreference Resolution (2021.naacl-main)
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| Challenge: | a neural event coreference model is based on a task of determining whether two event mentions refer to the same event . event coreferent tasks require nontrivial tasks such as identifying potential arguments and linking arguments to their event mention. |
| Approach: | They propose a neural event coreference model in which event coreference is jointly trained with five tasks. |
| Outcome: | The proposed model achieves state-of-the-art on the KBP 2017 event coreference dataset. |
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. |
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. |
Conundrums in Event Coreference Resolution: Making Sense of the State of the Art (2021.emnlp-main)
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| Challenge: | Recent years have seen the successful application of span-based neural models to entity-based information extraction tasks such as entity coreference resolution (CR) Existing event coreference resolvers focused on feature engineering are few and far between, let alone event corefers. |
| Approach: | They propose to adapt existing span-based event reference systems to event coreference by adapting the models originally developed for entity coreference to event CR. |
| Outcome: | The proposed model improves the representations of entity mentions in entity-based IE tasks compared to non-span models . |
A Context-Dependent Gated Module for Incorporating Symbolic Semantics into Event Coreference Resolution (2021.naacl-main)
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| Challenge: | Existing methods for event coreference resolution use symbolic features, but they are noisy and contain errors. |
| Approach: | They propose a context-dependent gated module to adaptively control the information flows from the input symbolic features. |
| Outcome: | The proposed model achieves state-of-the-art on two datasets: ACE 2005 and KBP 2016 . |
Automatic Data Acquisition for Event Coreference Resolution (2021.eacl-main)
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| Challenge: | lexical paraphrases and high precision rules informed by news discourse structure can be used to collect coreferential and non-coreferential event pairs from unlabeled English news articles. |
| Approach: | They propose to use lexical paraphrases and news discourse structure to automatically collect coreferential and non-coreferential event pairs from unlabeled English news articles. |
| Outcome: | The proposed model performs better than the supervised model on evaluation datasets with different event domains and text genres. |
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. |
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. |