On the Robustness of Document-Level Relation Extraction Models to Entity Name Variations (2024.findings-acl)
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| 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. |
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| 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 . |
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Towards Integration of Discriminability and Robustness for Document-Level Relation Extraction (2023.eacl-main)
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| 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 . |
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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. |
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| Challenge: | Named Entity Recognition (NER) models rely on superficial entity patterns for predictions, without considering evidence from the context. |
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| Challenge: | SpanBERT model is more robust than RoBERTa, despite having similar accuracy on unperturbed test data. |
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Robustness of Named-Entity Replacements for In-Context Learning (2023.findings-emnlp)
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Saeed Goodarzi, Nikhil Kagita, Dennis Minn, Shufan Wang, Roberto Dessi, Shubham Toshniwal, Adina Williams, Jack Lanchantin, Koustuv Sinha
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Re2-DocRED: Revisiting Revisited-DocRED for Joint Entity and Relation Extraction (2026.eacl-long)
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| Challenge: | Document-level Joint Entity and Relation Extraction benchmarks such as DocRED, Re-DocRED, and DocGNRE suffer from pervasive False Negatives (FN) |
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CaDRL: Document-level Relation Extraction via Context-aware Differentiable Rule Learning (2025.coling-main)
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Kunli Zhang, Pengcheng Wu, Bohan Yu, Kejun Wu, Aoze Zheng, Xiyang Huang, Chenkang Zhu, Min Peng, Hongying Zan, Yu Song
| Challenge: | Existing methods for document-level relation extraction (DocRE) lack logic and transparency. |
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Sentence-Level Resampling for Named Entity Recognition (2022.naacl-main)
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| Challenge: | named entity recognition (NER) tasks are often dominated by the majority of non-entity tokens in text . a data imbalance problem is causing the NER models to ignore named entities . |
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Revisiting DocRED - Addressing the False Negative Problem in Relation Extraction (2022.emnlp-main)
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| Challenge: | Using incomplete annotations, we find that false negative samples are prevalent in the DocRED dataset . we reannotate 4,053 documents in the dataset by adding the missed relation triples back to the original DocRED. |
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