Towards Integration of Discriminability and Robustness for Document-Level Relation Extraction (2023.eacl-main)
Copied to clipboard
| Challenge: | Document-level relation extraction (DocRE) predicts relations for entity pairs relying on context-dependent reasoning . a large number of annotation errors can make it difficult to distinguish large semantically close relations . |
| Approach: | They propose a loss function to improve discriminability and robustness for DocRE . they also propose supervised contrastive learning and negative label sampling strategy . |
| Outcome: | The proposed method achieves state-of-the-art results on the DocRED dataset and its recently cleaned version. |
Similar Papers
Document-Level Relation Extraction with Global Relations and Entity Pair Reasoning (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing document-level relation extraction models focus on individual entity pairs, limiting their ability to handle complex reasoning tasks. |
| Approach: | They propose a document-level relation extraction framework based on global relations and entity pair reasoning that captures fine-grained interactions between entity pairs. |
| Outcome: | The proposed framework outperforms existing models on widely-used datasets. |
On the Robustness of Document-Level Relation Extraction Models to Entity Name Variations (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing DocRE models which perform well may make more mistakes when merely changing the entity names in the document, hindering the generalization to novel entity names. |
| Approach: | They propose a pipeline to generate entity-renamed documents by replacing the original entity names with names from Wikidata. |
| Outcome: | The proposed pipeline generates entity-renamed documents by replacing the original entity names with names from Wikidata. |
ATGL: An Adaptive-Threshold Global Loss for Document-level Relation Extraction (2026.acl-long)
Copied to clipboard
| Challenge: | Document-level relation extraction (DocRE) aims to determine which relations hold between a given entity pair in a document. |
| Approach: | They propose a document-level relation extraction paradigm that decouples existing losses into independent positive and negative losses, which interact solely with a shared threshold. |
| Outcome: | The proposed model outperforms existing models on four datasets and achieves state-of-the-art results. |
Rethinking Document-Level Relation Extraction: A Reality Check (2023.findings-acl)
Copied to clipboard
| Challenge: | Recent efforts push up performance boundaries of document-level relation extraction (DocRE) but these efforts are not promising. |
| Approach: | They construct four types of entity mention attacks to examine model robustness . they also have a close check on model usability in a more realistic setting . |
| Outcome: | The proposed model is based on a strong or untenable assumption in common . the model is robust under four types of mention attacks and usable in a realistic setting . |
Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation (2022.findings-acl)
Copied to clipboard
| Challenge: | Document-level relation extraction (DocRE) is a more challenging task than sentence-level one. |
| Approach: | They propose a semi-supervised framework for document-level relation extraction with three components . they use an axial attention module for learning the interdependency among entity-pairs . |
| Outcome: | The proposed model outperforms baseline models on two DocRE datasets and outperformed previous models on human annotated data and distantly supervised data. |
Not Just Plain Text! Fuel Document-Level Relation Extraction with Explicit Syntax Refinement and Subsentence Modeling (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Document-level relation extraction (DocRE) aims to identify semantic labels among entities within a document. |
| Approach: | They propose a document-level relation extraction framework that captures and exploits instructive information by adding extra syntactic information into text representations. |
| Outcome: | The proposed framework outperforms existing methods on three benchmark datasets. |
Entity Pair-guided Relation Summarization and Retrieval in LLMs for Document-level Relation Extraction (2025.findings-naacl)
Copied to clipboard
| Challenge: | Document-level relation extraction (DocRE) aims to extract relations between entities in a document. |
| Approach: | They propose an entity pair-guided relation summarization and retrieval model for DocRE . the model uses entity pairs to guide relation summaries and retrievals . |
| Outcome: | The proposed model achieves state-of-the-art (SOTA) performance on three datasets. |
Uncertainty Guided Label Denoising for Document-level Distant Relation Extraction (2023.acl-long)
Copied to clipboard
| Challenge: | Document-level relation extraction (DocRE) aims to extract semantic relations between entities in a document. |
| Approach: | They propose a Document-level distant relation extraction framework with unreliable pseudo labels to denoise DS data. |
| Outcome: | The proposed framework outperforms strong baselines on two public datasets. |
Anaphor Assisted Document-Level Relation Extraction (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for document-level relation extraction are incomplete and lack anaphor for identifying relations between entities. |
| Approach: | They propose an Anaphor-Assisted (AA) framework for document-level relation extraction . they use a document or sentences as intermediate nodes to model cross-sentence entity interactions . |
| Outcome: | The proposed framework achieves state-of-the-art on the widely-used datasets. |
DocRED: A Large-Scale Document-Level Relation Extraction Dataset (P19-1)
Copied to clipboard
Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, Maosong Sun
| Challenge: | Existing relation extraction methods focus on extracting intra-sentence relations for single entities. |
| Approach: | They propose a relation extraction dataset from Wikipedia and Wikidata with three features . document-level relation extraction is a task to identify relational facts between entities . |
| Outcome: | The proposed dataset is the largest human-annotated dataset for document-level RE from plain text. |