500 citations · 936 across the 43 of their papers we have counts for
7 papers · 1 filter
ERASER: A Benchmark to Evaluate Rationalized NLP Models
Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani +4
State-of-the-art models in NLP are now predominantly based on deep neural networks that are opaque in terms of how they come to make predictions. This limitation has increased inte…
Learning to Identify Patients at Risk of Uncontrolled Hypertension Using Electronic Health Records Data
Ramin Mohammadi, Sarthak Jain, Stephen Agboola +3
Hypertension is a major risk factor for stroke, cardiovascular disease, and end-stage renal disease, and its prevalence is expected to rise dramatically. Effective hypertension man…
Predicting Annotation Difficulty to Improve Task Routing and Model Performance for Biomedical Information Extraction
Yinfei Yang, Oshin Agarwal, Chris Tar +2
Modern NLP systems require high-quality annotated data. In specialized domains, expert annotations may be prohibitively expensive. An alternative is to rely on crowdsourcing to red…
An Analysis of Attention over Clinical Notes for Predictive Tasks
Sarthak Jain, Ramin Mohammadi, Byron C. Wallace
The shift to electronic medical records (EMRs) has engendered research into machine learning and natural language technologies to analyze patient records, and to predict from these…
Inferring Which Medical Treatments Work from Reports of Clinical Trials
Eric Lehman, Jay DeYoung, Regina Barzilay +1
How do we know if a particular medical treatment actually works? Ideally one would consult all available evidence from relevant clinical trials. Unfortunately, such results are pri…
Attention is not Explanation
Sarthak Jain, Byron C. Wallace
Attention mechanisms have seen wide adoption in neural NLP models. In addition to improving predictive performance, these are often touted as affording transparency: models equippe…