Papers by Lis Pereira
QA-based Event Start-Points Ordering for Clinical Temporal Relation Annotation (2024.lrec-main)
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| Challenge: | Temporal relation annotation in the clinical domain is crucial but challenging due to its workload and the medical expertise required. |
| Approach: | They propose an annotation method that integrates event start-points ordering and question-answering as the annotation format. |
| Outcome: | The proposed method achieves a 0.72 F1 score and enables collaboration among medical experts and non-experts. |
AMR-RE: Abstract Meaning Representations for Retrieval-Based In-Context Learning in Relation Extraction (2025.naacl-srw)
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| Challenge: | Existing in-context learning methods for relation extraction often overlook entity relationships . Existing methods for RE prioritize language similarity over structural similarity . |
| Approach: | They propose an AMR-enhanced retrieval-based ICL method for relation extraction . their method retrieves in-context examples based on semantic structure similarity . |
| Outcome: | The proposed method outperforms baselines on four English RE datasets and in the more demanding unsupervised setting. |
Posterior Differential Regularization with f-divergence for Improving Model Robustness (2021.naacl-main)
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| Challenge: | Recent studies show that pre-trained models suffer catastrophic degradation in out-of-domain generalization to datasets with domain shift or adversarial scenarios. |
| Approach: | They propose to regularize the posterior difference between clean and noisy inputs by using a Jacobian regularization framework and a virtual adversarial training framework. |
| Outcome: | The proposed framework can improve model robustness in fully supervised and semi-supervised settings. |
Targeted Adversarial Training for Natural Language Understanding (2021.naacl-main)
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| Challenge: | Existing adversarial training approaches focus on making adversarials less expensive or regularizing rather than replacing the standard training objective. |
| Approach: | They propose an algorithm to introspect current mistakes and prioritize adversarial training steps to where the model errs the most. |
| Outcome: | The proposed algorithm improves adversarial training for natural language understanding by introspecting mistakes and prioritizing training steps to where the model errs the most. |