500 citations · 511 across the 7 of their papers we have counts for
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Influence Functions for Sequence Tagging Models
Sarthak Jain, Varun Manjunatha, Byron C. Wallace +1
Many language tasks (e.g., Named Entity Recognition, Part-of-Speech tagging, and Semantic Role Labeling) are naturally framed as sequence tagging problems. However, there has been…
Modular Self-Supervision for Document-Level Relation Extraction
Sheng Zhang, Cliff Wong, Naoto Usuyama +3
Extracting relations across large text spans has been relatively underexplored in NLP, but it is particularly important for high-value domains such as biomedicine, where obtaining…
Does BERT Pretrained on Clinical Notes Reveal Sensitive Data?
Eric Lehman, Sarthak Jain, Karl Pichotta +2
Large Transformers pretrained over clinical notes from Electronic Health Records (EHR) have afforded substantial gains in performance on predictive clinical tasks. The cost of trai…
An Empirical Comparison of Instance Attribution Methods for NLP
Pouya Pezeshkpour, Sarthak Jain, Byron C. Wallace +1
Widespread adoption of deep models has motivated a pressing need for approaches to interpret network outputs and to facilitate model debugging. Instance attribution methods constit…
SciREX: A Challenge Dataset for Document-Level Information Extraction
Sarthak Jain, Madeleine van Zuylen, Hannaneh Hajishirzi +1
Extracting information from full documents is an important problem in many domains, but most previous work focus on identifying relationships within a sentence or a paragraph. It i…
Learning to Faithfully Rationalize by Construction
Sarthak Jain, Sarah Wiegreffe, Yuval Pinter +1
In many settings it is important for one to be able to understand why a model made a particular prediction. In NLP this often entails extracting snippets of an input text `responsi…