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20182022
most citedExpansion via Prediction of Importance with Contextualization

72 citations · 164 across the 10 of their papers we have counts for

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cs.CL20218 cited

ToxCCIn: Toxic Content Classification with Interpretability

Tong Xiang, Sean MacAvaney, Eugene Yang +1

Despite the recent successes of transformer-based models in terms of effectiveness on a variety of tasks, their decisions often remain opaque to humans. Explanations are particular…

cs.CL2020

SLEDGE-Z: A Zero-Shot Baseline for COVID-19 Literature Search

Sean MacAvaney, Arman Cohan, Nazli Goharian

With worldwide concerns surrounding the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), there is a rapidly growing body of scientific literature on the virus. Clinici…

cs.CL2020

GUIR at SemEval-2020 Task 12: Domain-Tuned Contextualized Models for Offensive Language Detection

Sajad Sotudeh, Tong Xiang, Hao-Ren Yao +4

Offensive language detection is an important and challenging task in natural language processing. We present our submissions to the OffensEval 2020 shared task, which includes thre…

cs.CL20203 cited

Interaction Matching for Long-Tail Multi-Label Classification

Sean MacAvaney, Franck Dernoncourt, Walter Chang +2

We present an elegant and effective approach for addressing limitations in existing multi-label classification models by incorporating interaction matching, a concept shown to be u…

cs.CL2019

Ontology-Aware Clinical Abstractive Summarization

Sean MacAvaney, Sajad Sotudeh, Arman Cohan +3

Automatically generating accurate summaries from clinical reports could save a clinician's time, improve summary coverage, and reduce errors. We propose a sequence-to-sequence abst…

cs.CL2018

SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions

Arman Cohan, Bart Desmet, Andrew Yates +3

Mental health is a significant and growing public health concern. As language usage can be leveraged to obtain crucial insights into mental health conditions, there is a need for l…