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Adversarial training for multi-context joint entity and relation extraction

arXiv 1808.06876 ↗First posted 21 Aug 2018cs.CLcited by 29

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Abstract (arXiv)

Adversarial training (AT) is a regularization method that can be used to improve the robustness of neural network methods by adding small perturbations in the training data. We show how to use AT for the tasks of entity recognition and relation extraction. In particular, we demonstrate that applying AT to a general purpose baseline model for jointly extracting entities and relations, allows improving the state-of-the-art effectiveness on several datasets in different contexts (i.e., news, biomedical, and real estate data) and for different languages (English and Dutch).

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Aug 2018.

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Citing papers per year: 2021: 12; 2022: 7; 2023: 5; 2024: 3; 2025: 1; 2026: 1.

29 papers.

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  1. 2022e-CARE: a New Dataset for Exploring Explainable Causal Reasoningcited by 47
  2. 2024MTLoRA: A Low-Rank Adaptation Approach for Efficient Multi-Task Learningcited by 44
  3. 2024DINO-Foresight: Looking into the Future with DINOcited by 44
  4. 2021A Partition Filter Network for Joint Entity and Relation Extractioncited by 42
  5. 2022A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methodscited by 42
  6. 2021A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extractioncited by 26
  7. 2021A Survey on Extraction of Causal Relations from Natural Language Textcited by 25
  8. 2021Cross-Task Instance Representation Interactions and Label Dependencies for Joint Information Extraction with Graph Convolutional Networkscited by 22
  9. 2021Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced Collective Inferencecited by 19
  10. 2021Context-aware Adversarial Training for Name Regularity Bias in Named Entity Recognitioncited by 19
  11. 2022What do You Mean by Relation Extraction? A Survey on Datasets and Study on Scientific Relation Classificationcited by 17
  12. 2022TAGPRIME: A Unified Framework for Relational Structure Extractioncited by 15
17 more
  1. 2022Global Pointer: Novel Efficient Span-based Approach for Named Entity Recognitioncited by 15
  2. 2023Jointly Extracting Interventions, Outcomes, and Findings from RCT Reports with LLMscited by 10
  3. 2021Imposing Relation Structure in Language-Model Embeddings Using Contrastive Learningcited by 9
  4. 2021A Data Bootstrapping Recipe for Low Resource Multilingual Relation Classificationcited by 8
  5. 2023Unified Text Structuralization with Instruction-tuned Language Modelscited by 8
  6. 2021"Is depression related to cannabis?": A knowledge-infused model for Entity and Relation Extraction with Limited Supervisioncited by 7
  7. 2025TADFormer : Task-Adaptive Dynamic Transformer for Efficient Multi-Task Learningcited by 5
  8. 2021Leveraging Unlabeled Data for Entity-Relation Extraction through Probabilistic Constraint Satisfactioncited by 2
  9. 2021Separating Retention from Extraction in the Evaluation of End-to-end Relation Extractioncited by 2
  10. 2023EnCore: Fine-Grained Entity Typing by Pre-Training Entity Encoders on Coreference Chainscited by 2
  11. 2023JSEEGraph: Joint Structured Event Extraction as Graph Parsingcited by 2
  12. 2021Adversarial Training for Machine Reading Comprehension with Virtual Embeddingscited by 1
  13. 2022IDIAPers @ Causal News Corpus 2022: Extracting Cause-Effect-Signal Triplets via Pre-trained Autoregressive Language Modelcited by 1
  14. 2023SHINE: Syntax-augmented Hierarchical Interactive Encoder for Zero-shot Cross-lingual Information Extractioncited by 1
  15. 2022Joint Learning-based Causal Relation Extraction from Biomedical Literaturecited by 0
  16. 2024AutoIE: An Automated Framework for Information Extraction from Scientific Literaturecited by 0
  17. 2026A Joint Neural Baseline for Concept, Assertion, and Relation Extraction from Clinical Textcited by 0