Papers by Nabila Abraham
Meta-learning Pathologies from Radiology Reports using Variance Aware Prototypical Networks (2022.emnlp-industry)
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| Challenge: | Existing methods for fewshot learning require a large number of in-domain labeled examples for fine tuning. |
| Approach: | They propose to extend the Prototypical Networks for few-shot text classification by replacing Gaussian class prototypes with a regularization term that encourages the examples to be clustered near the appropriate class centroids. |
| Outcome: | The proposed method outperforms baselines on 13 public and 4 internal datasets and detects potential out-of-distribution (OOD) data points during deployment. |
Explaining the Effectiveness of Multi-Task Learning for Efficient Knowledge Extraction from Spine MRI Reports (2022.naacl-industry)
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Arijit Sehanobish, McCullen Sandora, Nabila Abraham, Jayashri Pawar, Danielle Torres, Anasuya Das, Murray Becker, Richard Herzog, Benjamin Odry, Ron Vianu
| Challenge: | Pretrained Transformer based models finetuned on domain specific corpora have changed the landscape of NLP but training or fine-tuning these models for individual tasks can be time consuming and resource intensive. |
| Approach: | They propose to use pretrained Transformer based models finetuned on domain specific corpora to train models for individual tasks. |
| Outcome: | The proposed model can match the performance of a task specific model when the task specific models show similar representations across all of their hidden layers and their gradients are aligned, i.e. their gradient follows the same direction. |