Papers by Fahmida Alam
Towards Realistic Few-Shot Relation Extraction: A New Meta Dataset and Evaluation (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for few-shot relation extraction are not realistic due to the large amount of training data required. |
| Approach: | They propose a meta dataset for few-shot relation extraction based on existing supervised relation extraction datasets and a few-shot form of the TACRED dataset. |
| Outcome: | The proposed methods perform poorly on the few-shot relation extraction task. |
Best of Both Worlds: A Pliable and Generalizable Neuro-Symbolic Approach for Relation Classification (2024.findings-naacl)
Copied to clipboard
| Challenge: | a novel neuro-symbolic architecture for relation classification combines rule-based methods with deep learning techniques. |
| Approach: | They propose a neuro-symbolic architecture for relation classification that combines rule-based methods with deep learning techniques. |
| Outcome: | The proposed approach outperforms state-of-the-art models in three out of four settings . human interventions boost the performance on the relation org:parents by as much as 26% relative improvement . |