Focus on what matters: Applying Discourse Coherence Theory to Cross Document Coreference (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches simplify by considering coreference only within document clusters, but this fails to handle inter-cluster coreference, common in many applications. |
| Approach: | They propose to model entities/events in a reader’s focus as a neighborhood within a learned latent embedding space which minimizes the distance between mentions and the centroids of their gold coreference clusters. |
| Outcome: | The proposed model achieves state-of-the-art for events and entities on the ECB+, Gun Violence, Football Coreference, and Cross-Domain Cross-DDocument Coreference corpora. |
Similar Papers
Employing Discourse Coherence Enhancement to Improve Cross-Document Event and Entity Coreference Resolution (2025.acl-long)
Copied to clipboard
| Challenge: | Existing work on cross-document coreference resolution focuses on within-document events and entities, but cross-doc mentions lack such critical contexts. |
| Approach: | They propose a task to enhance the discourse coherence between two cross-document mentions by adding coherent texts to a document to form a new coherent document. |
| Outcome: | The proposed method outperforms state-of-the-art baselines on three popular datasets. |
Enhancing Cross-Document Event Coreference Resolution by Discourse Structure and Semantic Information (2024.lrec-main)
Copied to clipboard
| 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. |
Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution (P19-1)
Copied to clipboard
| 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. |
Cross-Document Event Coreference Resolution on Discourse Structure (2023.emnlp-main)
Copied to clipboard
| Challenge: | Experimental results show that our proposed model outperforms several baselines and achieves the competitive performance with the start-of-the-art baselines. |
| Approach: | They propose to use discourse rhetorical structure constructor to construct tree structures to represent documents and a multi-layer perceptron to capture similarities of event mention pairs. |
| Outcome: | The proposed model outperforms baselines and achieves competitive performance with the start-of-the-art baselines. |
xCoRe: Cross-context Coreference Resolution (2025.emnlp-main)
Copied to clipboard
| Challenge: | Current coreference resolution systems are limited to short-to-medium-sized documents and struggle to scale to very long documents due to architectural limitations and implied memory costs. |
| Approach: | They propose a unified approach to coreference resolution that unifies two challenging settings . they use a pipeline that first identifies mentions, then creates clusters within individual contexts . |
| Outcome: | The proposed model achieves state-of-the-art results on cross-document benchmarks and strong performance on long-document data while retaining top-tier results on traditional datasets. |
Cross-document Coreference Resolution over Predicted Mentions (2021.findings-acl)
Copied to clipboard
| Challenge: | Cross-document coreference resolution has been under-explored in recent years . however, the challenge of cross-document resolution remains relatively under-studied . |
| Approach: | They propose a model for cross-document coreference resolution from raw text that extends a prominent withindocument corefer model to the CD setting. |
| Outcome: | The proposed model achieves competitive results for event and entity coreference resolution on gold mentions. |
Incorporating Temporal Coherence to Cross-Document Event Coreference Resolution (2026.acl-long)
Copied to clipboard
| Challenge: | Existing approaches focus on enhancing semantic coherence between event mentions, but they overlook the critical aspect of temporal coherency. |
| Approach: | They propose a Temporal Cohorence-driven event coreference framework that explicitly models temporal constraints by constructing a temporal event graph and a GNN to resolve conflicts. |
| Outcome: | Experiments on the ECB+, GVC, WEC, and ECb+META datasets show that CohTP outperforms state-of-the-art methods. |
Contrastive Representation Learning for Cross-Document Coreference Resolution of Events and Entities (2022.naacl-main)
Copied to clipboard
| Challenge: | Identifying related entities and events within and across documents is fundamental to natural language understanding. |
| Approach: | They propose an approach to entity and event coreference resolution using contrastive representation learning. |
| Outcome: | The proposed method achieves state-of-the-art results on key metrics on the ECB+ corpus and is competitive on others. |
2*n is better than n2: Decomposing Event Coreference Resolution into Two Tractable Problems (2023.findings-acl)
Copied to clipboard
| 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. |
Sequential Cross-Document Coreference Resolution (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing models for cross-document coreference resolution have been used for within-document entity coreference but have been relatively limited. |
| Approach: | They propose a model that extends the efficient sequential prediction paradigm for coreference resolution to cross-document settings and achieves competitive results for both entity and event coreference. |
| Outcome: | The proposed model achieves competitive results for entity and event coreference while minimizing error propagation in complex reasoning tasks. |