Papers by William Hogan
Open-world Semi-supervised Generalized Relation Discovery Aligned in a Real-world Setting (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to open-world relation extraction assume that all instances of unlabeled data belong to novel classes. |
| Approach: | They propose a method that classifies relations from known and novel classes within unlabeled data. |
| Outcome: | The proposed method outperforms existing methods on Open-world RE benchmarks. |
Fine-grained Contrastive Learning for Relation Extraction (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods assume all silver labels are accurate and treat them equally, but distant supervision is noisy–some silver labels more reliable than others. |
| Approach: | They propose a noise-aware contrastive learning approach that leverages fine-grained information about which silver labels are and are not noisy to improve the quality of learned relationship representations. |
| Outcome: | The proposed approach improves relation extraction performance over state-of-the-art methods on several RE benchmarks. |
READ: Improving Relation Extraction from an ADversarial Perspective (2024.findings-naacl)
Copied to clipboard
| 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. |