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20182023
most citedQuantifying Community Characteristics of Maternal Mortality Using Social Media

9 citations · 22 across the 7 of their papers we have counts for

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13 papers · 1 filter

cs.CL20232 cited

Systematic Evaluation of GPT-3 for Zero-Shot Personality Estimation

Adithya V Ganesan, Yash Kumar Lal, August Håkan Nilsson +1

Very large language models (LLMs) perform extremely well on a spectrum of NLP tasks in a zero-shot setting. However, little is known about their performance on human-level NLP prob…

cs.CL20231 cited

Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class Challenge

Vasudha Varadarajan, Swanie Juhng, Syeda Mahwish +4

While transformer-based systems have enabled greater accuracies with fewer training examples, data acquisition obstacles still persist for rare-class tasks -- when the class label…

cs.CL2023

Psychological Metrics for Dialog System Evaluation

Salvatore Giorgi, Shreya Havaldar, Farhan Ahmed +6

We present metrics for evaluating dialog systems through a psychologically-grounded "human" lens in which conversational agents express a diversity of both states (e.g., emotion) a…

cs.CL2021

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…

cs.CL20212 cited

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…

cs.CL20208 cited

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…