Adversarial training for multi-context joint entity and relation extraction (D18-1)
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| Challenge: | Existing models that use adversarial training (AT) have been used in various tasks such as parsing, POS tagging, relation extraction and translation. |
| Approach: | They propose to use adversarial training (AT) to regularize neural network methods by adding small perturbations to the input data. |
| Outcome: | The proposed model improves state-of-the-art on news, biomedical, and real estate datasets and for different languages. |
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READ: Improving Relation Extraction from an ADversarial Perspective (2024.findings-naacl)
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| Challenge: | Recent work in relation extraction (RE) has high generalization capability, but adversarial training methods rely on entities. |
| Approach: | They propose an adversarial training method specifically designed for relation extraction that introduces sequence- and token-level perturbations to the sample and uses a separate perturbation vocabulary to improve the search for entity and context perturbations. |
| Outcome: | The proposed method significantly improves accuracy and robustness in low-resource scenarios. |
Robust Multilingual Part-of-Speech Tagging via Adversarial Training (N18-1)
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| Challenge: | Adversarial training (AT) is a powerful regularization method for neural networks, aiming to achieve robustness to input perturbations. |
| Approach: | They propose and analyze a neural POS tagging model that exploits adversarial training by training on unmodified and adversarials. |
| Outcome: | The proposed model improves overall tagging accuracy and prevents over-fitting in low resource languages and boosts tabbing accuracy for rare / unseen words. |
R-AT: Regularized Adversarial Training for Natural Language Understanding (2022.findings-emnlp)
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| Challenge: | Currently, adversarial training is a popular and powerful regularization method in the natural language domain. |
| Approach: | They propose to regularize adversarial training via dropout by perturbing word embeddings . they find that R-AT can improve many models by reducing adversariality . |
| Outcome: | The proposed method can reduce the inconsistency between training and testing of models with dropout. |
Extracting Entities and Relations with Joint Minimum Risk Training (D18-1)
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| Challenge: | Existing methods for detecting entities and relations are limited by the complexity of the joint learning paradigm. |
| Approach: | They propose a joint learning paradigm based on minimum risk training . they implement a strong and simple neural network to execute the MRT . |
| Outcome: | The proposed model is able to achieve state-of-the-art in the extraction task on ACE05 and NYT datasets. |
Adversarial Multi-lingual Neural Relation Extraction (C18-1)
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| Challenge: | Existing models cannot capture consistency and diversity of relation patterns in different languages. |
| Approach: | They propose an adversarial multi-lingual neural relation extraction model which considers consistency and diversity among languages. |
| Outcome: | The proposed model outperforms the state-of-the-art models on real-world datasets. |
Knowing False Negatives: An Adversarial Training Method for Distantly Supervised Relation Extraction (2021.emnlp-main)
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| Challenge: | Existing methods for relation extraction ignore the incompleteness of existing knowledge bases . current methods are too weak and cause noises when training and testing are not based on training data. |
| Approach: | They propose a method to automatically align unstructured text with relation instances in a knowledge base . they use heuristics to leverage the memory mechanism of deep neural networks to find out possible FN samples . |
| Outcome: | Experiments on two wildly-used benchmark datasets show the effectiveness of the proposed method. |
Enhancing Relation Extraction via Adversarial Multi-task Learning (2022.lrec-1)
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| Challenge: | Existing studies have focused on re-modeling the given NEs and thus lead to inferior results when NE is sometimes ambiguous. |
| Approach: | They propose a relation extraction model with two training stages that uses adversarial multi-task learning to recover the given NEs. |
| Outcome: | The proposed model improves on two English benchmark datasets and shows state-of-the-art performance. |
What’s in a Domain? Learning Domain-Robust Text Representations using Adversarial Training (N18-2)
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| Challenge: | a key roadblock is application to new domains, unseen in training. |
| Approach: | They propose a method to optimise in- and out-of-domain accuracy by combing domain-specific and domain-general components with adversarial training for domain. |
| Outcome: | The proposed method improves on domain adaptation and domain-adversarial training. |
Don’t Retrain, Just Rewrite: Countering Adversarial Perturbations by Rewriting Text (2023.acl-long)
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| Challenge: | ATINTER model can be used to rewrite adversarial inputs to make them non-adversarial . if undefended, model should maintain good task performance and effectively mitigate adversarials . |
| Approach: | They propose a model that intercepts adversarial inputs and learns to rewrite them . they show that it provides better adversarial robustness than existing defense approaches . |
| Outcome: | The proposed model improves adversarial robustness without compromising task accuracy on a sentiment classification dataset. |
ENPAR:Enhancing Entity and Entity Pair Representations for Joint Entity Relation Extraction (2021.eacl-main)
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| Challenge: | Existing methods for joint entity relation extraction use multitask learning frameworks, but annotations for additional tasks are hard to obtain. |
| Approach: | They propose a pre-training method to improve the joint extraction performance with just extra entity annotations. |
| Outcome: | The proposed method outperforms existing methods on ACE05, SciERC, and NYT and outperformed BERT on other tasks. |