102 citations · 359 across the 20 of their papers we have counts for
4 papers · 1 filter
Quantifying the Task-Specific Information in Text-Based Classifications
Zining Zhu, Aparna Balagopalan, Marzyeh Ghassemi +1
Recently, neural natural language models have attained state-of-the-art performance on a wide variety of tasks, but the high performance can result from superficial, surface-level…
Multiple Sclerosis Severity Classification From Clinical Text
Alister D Costa, Stefan Denkovski, Michal Malyska +5
Multiple Sclerosis (MS) is a chronic, inflammatory and degenerative neurological disease, which is monitored by a specialist using the Expanded Disability Status Scale (EDSS) and r…
SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness
Nathan Ng, Kyunghyun Cho, Marzyeh Ghassemi
Models that perform well on a training domain often fail to generalize to out-of-domain (OOD) examples. Data augmentation is a common method used to prevent overfitting and improve…
Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings
Haoran Zhang, Amy X. Lu, Mohamed Abdalla +2
In this work, we examine the extent to which embeddings may encode marginalized populations differently, and how this may lead to a perpetuation of biases and worsened performance…