9 citations · 19 across the 5 of their papers we have counts for
12 papers
MeLT: Message-Level Transformer with Masked Document Representations as Pre-Training for Stance Detection
Matthew Matero, Nikita Soni, Niranjan Balasubramanian +1
Much of natural language processing is focused on leveraging large capacity language models, typically trained over single messages with a task of predicting one or more tokens. Ho…
On the Distribution, Sparsity, and Inference-time Quantization of Attention Values in Transformers
Tianchu Ji, Shraddhan Jain, Michael Ferdman +3
How much information do NLP tasks really need from a transformer's attention mechanism at application-time (inference)? From recent work, we know that there is sparsity in transfor…
World Trade Center responders in their own words: Predicting PTSD symptom trajectories with AI-based language analyses of interviews
Youngseo Son, Sean A. P. Clouston, Roman Kotov +4
Background: Oral histories from 9/11 responders to the World Trade Center (WTC) attacks provide rich narratives about distress and resilience. Artificial Intelligence (AI) models p…
Detecting Emerging Symptoms of COVID-19 using Context-based Twitter Embeddings
Roshan Santosh, H. Andrew Schwartz, Johannes C. Eichstaedt +2
In this paper, we present an iterative graph-based approach for the detection of symptoms of COVID-19, the pathology of which seems to be evolving. More generally, the method can b…
Quantifying Community Characteristics of Maternal Mortality Using Social Media
Rediet Abebe, Salvatore Giorgi, Anna Tedijanto +2
While most mortality rates have decreased in the US, maternal mortality has increased and is among the highest of any OECD nation. Extensive public health research is ongoing to be…
Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview
Deven Shah, H. Andrew Schwartz, Dirk Hovy
An increasing number of works in natural language processing have addressed the effect of bias on the predicted outcomes, introducing mitigation techniques that act on different pa…