Search Results for author: Lyle Ungar

Found 83 papers, 18 papers with code

Nonsuicidal Self-Injury and Substance Use Disorders: A Shared Language of Addiction

no code implementations NAACL (CLPsych) 2022 Salvatore Giorgi, McKenzie Himelein-Wachowiak, Daniel Habib, Lyle Ungar, Brenda Curtis

To this end, we build a set of LDA topics across both NSSI and SUD Reddit users and show that shared language across the two domains includes SUD recovery language in addition to other themes common to support forums (e. g., requests for help and gratitude).

Measuring the Language of Self-Disclosure across Corpora

no code implementations Findings (ACL) 2022 Ann-Katrin Reuel, Sebastian Peralta, João Sedoc, Garrick Sherman, Lyle Ungar

Being able to reliably estimate self-disclosure – a key component of friendship and intimacy – from language is important for many psychology studies.

Item Response Theory for Efficient Human Evaluation of Chatbots

no code implementations EMNLP (Eval4NLP) 2020 João Sedoc, Lyle Ungar

Conversational agent quality is currently assessed using human evaluation, and often requires an exorbitant number of comparisons to achieve statistical significance.

Chatbot

Personalized Assignment to One of Many Treatment Arms via Regularized and Clustered Joint Assignment Forests

no code implementations1 Nov 2023 Rahul Ladhania, Jann Spiess, Lyle Ungar, Wenbo Wu

We consider learning personalized assignments to one of many treatment arms from a randomized controlled trial.

Clustering

Comparing Styles across Languages

1 code implementation11 Oct 2023 Shreya Havaldar, Matthew Pressimone, Eric Wong, Lyle Ungar

Understanding how styles differ across languages is advantageous for training both humans and computers to generate culturally appropriate text.

TopEx: Topic-based Explanations for Model Comparison

no code implementations1 Jun 2023 Shreya Havaldar, Adam Stein, Eric Wong, Lyle Ungar

Meaningfully comparing language models is challenging with current explanation methods.

Conceptor-Aided Debiasing of Large Language Models

no code implementations20 Nov 2022 Li S. Yifei, Lyle Ungar, João Sedoc

We propose two methods of applying conceptors (1) bias subspace projection by post-processing by the conceptor NOT operation; and (2) a new architecture, conceptor-intervened BERT (CI-BERT), which explicitly incorporates the conceptor projection into all layers during training.

Language Modelling

StyLEx: Explaining Style Using Human Lexical Annotations

1 code implementation14 Oct 2022 Shirley Anugrah Hayati, Kyumin Park, Dheeraj Rajagopal, Lyle Ungar, Dongyeop Kang

Large pre-trained language models have achieved impressive results on various style classification tasks, but they often learn spurious domain-specific words to make predictions (Hayati et al., 2021).

Sentence

Empathic Conversations: A Multi-level Dataset of Contextualized Conversations

no code implementations25 May 2022 Damilola Omitaomu, Shabnam Tafreshi, Tingting Liu, Sven Buechel, Chris Callison-Burch, Johannes Eichstaedt, Lyle Ungar, João Sedoc

Hence, we collected detailed characterization of the participants' traits, their self-reported empathetic response to news articles, their conversational partner other-report, and turn-by-turn third-party assessments of the level of self-disclosure, emotion, and empathy expressed.

Different Affordances on Facebook and SMS Text Messaging Do Not Impede Generalization of Language-Based Predictive Models

no code implementations3 Feb 2022 Tingting Liu, Salvatore Giorgi, Xiangyu Tao, Sharath Chandra Guntuku, Douglas Bellew, Brenda Curtis, Lyle Ungar

Adaptive mobile device-based health interventions often use machine learning models trained on non-mobile device data, such as social media text, due to the difficulty and high expense of collecting large text message (SMS) data.

Domain Adaptation

Social Media Reveals Urban-Rural Differences in Stress across China

1 code implementation19 Oct 2021 Jesse Cui, Tingdan Zhang, Kokil Jaidka, Dandan Pang, Garrick Sherman, Vinit Jakhetiya, Lyle Ungar, Sharath Chandra Guntuku

This paper studies linguistic differences in the experiences and expressions of stress in urban-rural China from Weibo posts from over 65, 000 users across 329 counties using hierarchical mixed-effects models.

WikiTalkEdit: A Dataset for modeling Editors' behaviors on Wikipedia

no code implementations NAACL 2021 Kokil Jaidka, Andrea Ceolin, Iknoor Singh, Niyati Chhaya, Lyle Ungar

We show how the data supports the classic understanding of style matching, where positive emotion and the use of first-person pronouns predict a positive emotional change in a Wikipedia contributor.

Predicting Responses to Psychological Questionnaires from Participants' Social Media Posts and Question Text Embeddings

no code implementations Findings of the Association for Computational Linguistics 2020 Huy Vu, Suhaib Abdurahman, Sudeep Bhatia, Lyle Ungar

Finally, as a side contribution, the success of our model also suggests a new approach to study survey questions using NLP tools such as text embeddings rather than response data used in traditional methods.

Toward Micro-Dialect Identification in Diaglossic and Code-Switched Environments

1 code implementation EMNLP 2020 Muhammad Abdul-Mageed, Chiyu Zhang, AbdelRahim Elmadany, Lyle Ungar

Although the prediction of dialects is an important language processing task, with a wide range of applications, existing work is largely limited to coarse-grained varieties.

Dialect Identification Language Modelling +1

Studying Politeness across Cultures Using English Twitter and Mandarin Weibo

1 code implementation6 Aug 2020 Mingyang Li, Louis Hickman, Louis Tay, Lyle Ungar, Sharath Chandra Guntuku

We study the linguistic features associated with politeness across US English and Mandarin Chinese.

Social and Information Networks Computers and Society

Generalized SHAP: Generating multiple types of explanations in machine learning

no code implementations12 Jun 2020 Dillon Bowen, Lyle Ungar

Many important questions about a model cannot be answered just by explaining how much each feature contributes to its output.

BIG-bench Machine Learning General Classification

Learning Word Ratings for Empathy and Distress from Document-Level User Responses

no code implementations LREC 2020 João Sedoc, Sven Buechel, Yehonathan Nachmany, Anneke Buffone, Lyle Ungar

The underlying problem of learning word ratings from higher-level supervision has to date only been addressed in an ad hoc fashion and has not used deep learning methods.

Clustering Emotion Recognition

Correcting Sociodemographic Selection Biases for Population Prediction from Social Media

1 code implementation10 Nov 2019 Salvatore Giorgi, Veronica Lynn, Keshav Gupta, Farhan Ahmed, Sandra Matz, Lyle Ungar, H. Andrew Schwartz

However, social media users are not typically a representative sample of the intended population -- a "selection bias".

Selection bias

Sentence-Level BERT and Multi-Task Learning of Age and Gender in Social Media

no code implementations2 Nov 2019 Muhammad Abdul-Mageed, Chiyu Zhang, Arun Rajendran, AbdelRahim Elmadany, Michael Przystupa, Lyle Ungar

In this work we exploit a newly-created Arabic dataset with ground truth age and gender labels to learn these attributes both individually and in a multi-task setting at the sentence level.

Multi-Task Learning Sentence

The Role of Protected Class Word Lists in Bias Identification of Contextualized Word Representations

no code implementations WS 2019 Jo{\~a}o Sedoc, Lyle Ungar

Systemic bias in word embeddings has been widely reported and studied, and efforts made to debias them; however, new contextualized embeddings such as ELMo and BERT are only now being similarly studied.

Word Embeddings

Conceptor Debiasing of Word Representations Evaluated on WEAT

no code implementations WS 2019 Saket Karve, Lyle Ungar, João Sedoc

Bias in word embeddings such as Word2Vec has been widely investigated, and many efforts made to remove such bias.

Word Embeddings

ChatEval: A Tool for Chatbot Evaluation

no code implementations NAACL 2019 Jo{\~a}o Sedoc, Daphne Ippolito, Arun Kirubarajan, Jai Thirani, Lyle Ungar, Chris Callison-Burch

We introduce a unified framework for human evaluation of chatbots that augments existing tools and provides a web-based hub for researchers to share and compare their dialog systems.

Chatbot Open-Domain Dialog

Understanding and Measuring Psychological Stress using Social Media

1 code implementation19 Nov 2018 Sharath Chandra Guntuku, Anneke Buffone, Kokil Jaidka, Johannes Eichstaedt, Lyle Ungar

In this paper, we explore the language of psychological stress with a dataset of 601 social media users, who answered the Perceived Stress Scale questionnaire and also consented to share their Facebook and Twitter data.

Domain Adaptation

Identifying Locus of Control in Social Media Language

no code implementations EMNLP 2018 Masoud Rouhizadeh, Kokil Jaidka, Laura Smith, H. Andrew Schwartz, Anneke Buffone, Lyle Ungar

Individuals express their locus of control, or {``}control{''}, in their language when they identify whether or not they are in control of their circumstances.

Modeling Empathy and Distress in Reaction to News Stories

1 code implementation EMNLP 2018 Sven Buechel, Anneke Buffone, Barry Slaff, Lyle Ungar, João Sedoc

Computational detection and understanding of empathy is an important factor in advancing human-computer interaction.

Unsupervised Morphology Learning with Statistical Paradigms

1 code implementation COLING 2018 Hongzhi Xu, Mitchell Marcus, Charles Yang, Lyle Ungar

This paper describes an unsupervised model for morphological segmentation that exploits the notion of paradigms, which are sets of morphological categories (e. g., suffixes) that can be applied to a homogeneous set of words (e. g., nouns or verbs).

Information Retrieval Segmentation +1

User-Level Race and Ethnicity Predictors from Twitter Text

no code implementations COLING 2018 Daniel Preo{\c{t}}iuc-Pietro, Lyle Ungar

User demographic inference from social media text has the potential to improve a range of downstream applications, including real-time passive polling or quantifying demographic bias.

Diachronic degradation of language models: Insights from social media

no code implementations ACL 2018 Kokil Jaidka, Niyati Chhaya, Lyle Ungar

It asks the question: given that the social media platform and its users remain the same, how is language changing over time?

Word Embeddings

Current and Future Psychological Health Prediction using Language and Socio-Demographics of Children for the CLPysch 2018 Shared Task

no code implementations WS 2018 Sharath Ch Guntuku, ra, Salvatore Giorgi, Lyle Ungar

The goal of the shared task was to use childhood language as a marker for both current and future psychological health over individual lifetimes.

Clustering

Multiscale Hidden Markov Models For Covariance Prediction

no code implementations ICLR 2018 João Sedoc, Jordan Rodu, Dean Foster, Lyle Ungar

This paper presents a novel variant of hierarchical hidden Markov models (HMMs), the multiscale hidden Markov model (MSHMM), and an associated spectral estimation and prediction scheme that is consistent, finds global optima, and is computationally efficient.

Neural Tree Transducers for Tree to Tree Learning

no code implementations ICLR 2018 João Sedoc, Dean Foster, Lyle Ungar

We introduce a novel approach to tree-to-tree learning, the neural tree transducer (NTT), a top-down depth first context-sensitive tree decoder, which is paired with recursive neural encoders.

Decoder

Domain Adaptation from User-level Facebook Models to County-level Twitter Predictions

no code implementations IJCNLP 2017 Daniel Rieman, Kokil Jaidka, H. Andrew Schwartz, Lyle Ungar

Several studies have demonstrated how language models of user attributes, such as personality, can be built by using the Facebook language of social media users in conjunction with their responses to psychology questionnaires.

Domain Adaptation

DLATK: Differential Language Analysis ToolKit

no code implementations EMNLP 2017 H. Andrew Schwartz, Salvatore Giorgi, Maarten Sap, Patrick Crutchley, Lyle Ungar, Johannes Eichstaedt

We present Differential Language Analysis Toolkit (DLATK), an open-source python package and command-line tool developed for conducting social-scientific language analyses.

General Classification

Domain Aware Neural Dialog System

no code implementations2 Aug 2017 Sajal Choudhary, Prerna Srivastava, Lyle Ungar, João Sedoc

We investigate the task of building a domain aware chat system which generates intelligent responses in a conversation comprising of different domains.

Enterprise to Computer: Star Trek chatbot

1 code implementation2 Aug 2017 Grishma Jena, Mansi Vashisht, Abheek Basu, Lyle Ungar, João Sedoc

In this work, we propose a design for a chatbot that captures the "style" of Star Trek by incorporating references from the show along with peculiar tones of the fictional characters therein.

Chatbot Decoder

Deriving Verb Predicates By Clustering Verbs with Arguments

no code implementations1 Aug 2017 Joao Sedoc, Derry Wijaya, Masoud Rouhizadeh, Andy Schwartz, Lyle Ungar

Hand-built verb clusters such as the widely used Levin classes (Levin, 1993) have proved useful, but have limited coverage.

Clustering

Semantic Word Clusters Using Signed Spectral Clustering

no code implementations ACL 2017 Jo{\~a}o Sedoc, Jean Gallier, Dean Foster, Lyle Ungar

For spectral clustering using such word embeddings, words are points in a vector space where synonyms are linked with positive weights, while antonyms are linked with negative weights.

Clustering Graph Clustering +3

EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks

no code implementations ACL 2017 Muhammad Abdul-Mageed, Lyle Ungar

Accurate detection of emotion from natural language has applications ranging from building emotional chatbots to better understanding individuals and their lives.

Decision Making

Beyond Binary Labels: Political Ideology Prediction of Twitter Users

no code implementations ACL 2017 Daniel Preo{\c{t}}iuc-Pietro, Ye Liu, Daniel Hopkins, Lyle Ungar

Automatic political orientation prediction from social media posts has to date proven successful only in distinguishing between publicly declared liberals and conservatives in the US.

Latent Human Traits in the Language of Social Media: An Open-Vocabulary Approach

no code implementations22 May 2017 Vivek Kulkarni, Margaret L. Kern, David Stillwell, Michal Kosinski, Sandra Matz, Lyle Ungar, Steven Skiena, H. Andrew Schwartz

Taking advantage of linguistic information available through Facebook, we study the process of inferring a new set of potential human traits based on unprompted language use.

Predicting Emotional Word Ratings using Distributional Representations and Signed Clustering

no code implementations EACL 2017 Jo{\~a}o Sedoc, Daniel Preo{\c{t}}iuc-Pietro, Lyle Ungar

Inferring the emotional content of words is important for text-based sentiment analysis, dialogue systems and psycholinguistics, but word ratings are expensive to collect at scale and across languages or domains.

Clustering Position +2

Semantic Word Clusters Using Signed Normalized Graph Cuts

1 code implementation20 Jan 2016 João Sedoc, Jean Gallier, Lyle Ungar, Dean Foster

Vector space representations of words capture many aspects of word similarity, but such methods tend to make vector spaces in which antonyms (as well as synonyms) are close to each other.

Clustering Word Similarity

Faster Ridge Regression via the Subsampled Randomized Hadamard Transform

no code implementations NeurIPS 2013 Yichao Lu, Paramveer Dhillon, Dean P. Foster, Lyle Ungar

We propose a fast algorithm for ridge regression when the number of features is much larger than the number of observations ($p \gg n$).

regression

New Subsampling Algorithms for Fast Least Squares Regression

no code implementations NeurIPS 2013 Paramveer Dhillon, Yichao Lu, Dean P. Foster, Lyle Ungar

We address the problem of fast estimation of ordinary least squares (OLS) from large amounts of data ($n \gg p$).

regression

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