98 citations · 255 across the 21 of their papers we have counts for
22 papers
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…
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…
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…
Robust language-based mental health assessments in time and space through social media
Siddharth Mangalik, Johannes C. Eichstaedt, Salvatore Giorgi +8
Compared to physical health, population mental health measurement in the U.S. is very coarse-grained. Currently, in the largest population surveys, such as those carried out by the…
Human Language Modeling
Nikita Soni, Matthew Matero, Niranjan Balasubramanian +1
Natural language is generated by people, yet traditional language modeling views words or documents as if generated independently. Here, we propose human language modeling (HuLM),…
Evaluating Contextual Embeddings and their Extraction Layers for Depression Assessment
Matthew Matero, Albert Hung, H. Andrew Schwartz
Recent works have demonstrated ability to assess aspects of mental health from personal discourse. At the same time, pre-trained contextual word embedding models have grown to domi…
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…
Empirical Evaluation of Pre-trained Transformers for Human-Level NLP: The Role of Sample Size and Dimensionality
Adithya V Ganesan, Matthew Matero, Aravind Reddy Ravula +2
In human-level NLP tasks, such as predicting mental health, personality, or demographics, the number of observations is often smaller than the standard 768+ hidden state sizes of e…
Discourse Relation Embeddings: Representing the Relations between Discourse Segments in Social Media
Youngseo Son, Vasudha Varadarajan, H Andrew Schwartz
Discourse relations are typically modeled as a discrete class that characterizes the relation between segments of text (e.g. causal explanations, expansions). However, such predefi…
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…
Correcting Sociodemographic Selection Biases for Population Prediction from Social Media
Salvatore Giorgi, Veronica Lynn, Keshav Gupta +4
Social media is increasingly used for large-scale population predictions, such as estimating community health statistics. However, social media users are not typically a representa…
Causal Explanation Analysis on Social Media
Youngseo Son, Nipun Bayas, H. Andrew Schwartz
Understanding causal explanations - reasons given for happenings in one's life - has been found to be an important psychological factor linked to physical and mental health. Causal…
Learning Emotion from 100 Observations: Unexpected Robustness of Deep Learning under Strong Data Limitations
Sven Buechel, João Sedoc, H. Andrew Schwartz +1
One of the major downsides of Deep Learning is its supposed need for vast amounts of training data. As such, these techniques appear ill-suited for NLP areas where annotated data i…
The Remarkable Benefit of User-Level Aggregation for Lexical-based Population-Level Predictions
Salvatore Giorgi, Daniel Preotiuc-Pietro, Anneke Buffone +3
Nowcasting based on social media text promises to provide unobtrusive and near real-time predictions of community-level outcomes. These outcomes are typically regarding people, but…
Residualized Factor Adaptation for Community Social Media Prediction Tasks
Mohammadzaman Zamani, H. Andrew Schwartz, Veronica E. Lynn +2
Predictive models over social media language have shown promise in capturing community outcomes, but approaches thus far largely neglect the socio-demographic context (e.g. age, ed…
Predicting Human Trustfulness from Facebook Language
Mohammadzaman Zamani, Anneke Buffone, H. Andrew Schwartz
Trustfulness -- one's general tendency to have confidence in unknown people or situations -- predicts many important real-world outcomes such as mental health and likelihood to coo…
Using Search Queries to Understand Health Information Needs in Africa
Rediet Abebe, Shawndra Hill, Jennifer Wortman Vaughan +2
The lack of comprehensive, high-quality health data in developing nations creates a roadblock for combating the impacts of disease. One key challenge is understanding the health in…
Latent Human Traits in the Language of Social Media: An Open-Vocabulary Approach
Vivek Kulkarni, Margaret L. Kern, David Stillwell +5
Over the past century, personality theory and research has successfully identified core sets of characteristics that consistently describe and explain fundamental differences in th…