Search Results for author: Fred Morstatter

Found 30 papers, 12 papers with code

The Unequal Opportunities of Large Language Models: Revealing Demographic Bias through Job Recommendations

no code implementations3 Aug 2023 Abel Salinas, Parth Vipul Shah, Yuzhong Huang, Robert McCormack, Fred Morstatter

Our study highlights the importance of measuring the bias of LLMs in downstream applications to understand the potential for harm and inequitable outcomes.

Modeling Cross-Cultural Pragmatic Inference with Codenames Duet

1 code implementation4 Jun 2023 Omar Shaikh, Caleb Ziems, William Held, Aryan J. Pariani, Fred Morstatter, Diyi Yang

Prior work uses simple reference games to test models of pragmatic reasoning, often with unidentified speakers and listeners.

Contextualizing Argument Quality Assessment with Relevant Knowledge

no code implementations20 May 2023 Darshan Deshpande, Zhivar Sourati, Filip Ilievski, Fred Morstatter

Automatic assessment of the quality of arguments has been recognized as a challenging task with significant implications for misinformation and targeted speech.


Temporal Knowledge Graph Forecasting Without Knowledge Using In-Context Learning

no code implementations17 May 2023 Dong-Ho Lee, Kian Ahrabian, Woojeong Jin, Fred Morstatter, Jay Pujara

This shows that prior semantic knowledge is unnecessary; instead, LLMs can leverage the existing patterns in the context to achieve such performance.

Noise Audits Improve Moral Foundation Classification

no code implementations13 Oct 2022 Negar Mokhberian, Frederic R. Hopp, Bahareh Harandizadeh, Fred Morstatter, Kristina Lerman

Morality classification relies on human annotators to label the moral expressions in text, which provides training data to achieve state-of-the-art performance.

Classification Cultural Vocal Bursts Intensity Prediction

Robust Conversational Agents against Imperceptible Toxicity Triggers

1 code implementation NAACL 2022 Ninareh Mehrabi, Ahmad Beirami, Fred Morstatter, Aram Galstyan

Existing work to generate such attacks is either based on human-generated attacks which is costly and not scalable or, in case of automatic attacks, the attack vector does not conform to human-like language, which can be detected using a language model loss.

Language Modelling Text Generation

"Stop Asian Hate!" : Refining Detection of Anti-Asian Hate Speech During the COVID-19 Pandemic

no code implementations4 Dec 2021 Huy Nghiem, Fred Morstatter

We demonstrate that we are able to identify hate speech that is systematically missed by established hate speech detectors.

Hate Speech Detection

Keyword Assisted Embedded Topic Model

1 code implementation22 Nov 2021 Bahareh Harandizadeh, J. Hunter Priniski, Fred Morstatter

By illuminating latent structures in a corpus of text, topic models are an essential tool for categorizing, summarizing, and exploring large collections of documents.

Topic Models Word Embeddings

AutoTriggER: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction

no code implementations10 Sep 2021 Dong-Ho Lee, Ravi Kiran Selvam, Sheikh Muhammad Sarwar, Bill Yuchen Lin, Fred Morstatter, Jay Pujara, Elizabeth Boschee, James Allan, Xiang Ren

Deep neural models for named entity recognition (NER) have shown impressive results in overcoming label scarcity and generalizing to unseen entities by leveraging distant supervision and auxiliary information such as explanations.

Low Resource Named Entity Recognition named-entity-recognition +2

Attributing Fair Decisions with Attention Interventions

1 code implementation NAACL (TrustNLP) 2022 Ninareh Mehrabi, Umang Gupta, Fred Morstatter, Greg Ver Steeg, Aram Galstyan

The widespread use of Artificial Intelligence (AI) in consequential domains, such as healthcare and parole decision-making systems, has drawn intense scrutiny on the fairness of these methods.

Decision Making Fairness

Analyzing Race and Country of Citizenship Bias in Wikidata

no code implementations11 Aug 2021 Zaina Shaik, Filip Ilievski, Fred Morstatter

Through this analysis, we discovered that there is an overrepresentation of white individuals and those with citizenship in Europe and North America; the rest of the groups are generally underrepresented.

Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources

no code implementations EMNLP 2021 Ninareh Mehrabi, Pei Zhou, Fred Morstatter, Jay Pujara, Xiang Ren, Aram Galstyan

In addition, we analyze two downstream models that use ConceptNet as a source for commonsense knowledge and find the existence of biases in those models as well.

Models, Markets, and the Forecasting of Elections

no code implementations6 Feb 2021 Rajiv Sethi, Julie Seager, Emily Cai, Daniel M. Benjamin, Fred Morstatter

We examine probabilistic forecasts for battleground states in the 2020 US presidential election, using daily data from two sources over seven months: a model published by The Economist, and prices from the PredictIt exchange.

Exacerbating Algorithmic Bias through Fairness Attacks

1 code implementation16 Dec 2020 Ninareh Mehrabi, Muhammad Naveed, Fred Morstatter, Aram Galstyan

Algorithmic fairness has attracted significant attention in recent years, with many quantitative measures suggested for characterizing the fairness of different machine learning algorithms.

Adversarial Attack BIG-bench Machine Learning +2

One-shot Learning for Temporal Knowledge Graphs

no code implementations AKBC 2021 Mehrnoosh Mirtaheri, Mohammad Rostami, Xiang Ren, Fred Morstatter, Aram Galstyan

Most real-world knowledge graphs are characterized by a long-tail relation frequency distribution where a significant fraction of relations occurs only a handful of times.

Knowledge Graphs Link Prediction +1

Leveraging Clickstream Trajectories to Reveal Low-Quality Workers in Crowdsourced Forecasting Platforms

no code implementations4 Sep 2020 Akira Matsui, Emilio Ferrara, Fred Morstatter, Andres Abeliuk, Aram Galstyan

In this study, we propose the use of a computational framework to identify clusters of underperforming workers using clickstream trajectories.

Autonomous Driving Clustering

Statistical Equity: A Fairness Classification Objective

1 code implementation14 May 2020 Ninareh Mehrabi, Yuzhong Huang, Fred Morstatter

We formalize our definition of fairness, and motivate it with its appropriate contexts.

Classification Fairness +1

ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data

no code implementations ACL 2021 Woojeong Jin, Rahul Khanna, Suji Kim, Dong-Ho Lee, Fred Morstatter, Aram Galstyan, Xiang Ren

In this work, we aim to formulate a task, construct a dataset, and provide benchmarks for developing methods for event forecasting with large volumes of unstructured text data.

Knowledge Graphs Language Modelling +5

Man is to Person as Woman is to Location: Measuring Gender Bias in Named Entity Recognition

1 code implementation24 Oct 2019 Ninareh Mehrabi, Thamme Gowda, Fred Morstatter, Nanyun Peng, Aram Galstyan

We study the bias in several state-of-the-art named entity recognition (NER) models---specifically, a difference in the ability to recognize male and female names as PERSON entity types.

named-entity-recognition Named Entity Recognition +1

A Survey on Bias and Fairness in Machine Learning

2 code implementations23 Aug 2019 Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, Aram Galstyan

With the commercialization of these systems, researchers are becoming aware of the biases that these applications can contain and have attempted to address them.

BIG-bench Machine Learning Fairness

Identifying and Analyzing Cryptocurrency Manipulations in Social Media

1 code implementation4 Feb 2019 Mehrnoosh Mirtaheri, Sami Abu-El-Haija, Fred Morstatter, Greg Ver Steeg, Aram Galstyan

Because of the speed and relative anonymity offered by social platforms such as Twitter and Telegram, social media has become a preferred platform for scammers who wish to spread false hype about the cryptocurrency they are trying to pump.

Cross-Platform Emoji Interpretation: Analysis, a Solution, and Applications

no code implementations14 Sep 2017 Fred Morstatter, Kai Shu, Suhang Wang, Huan Liu

We apply our solution to sentiment analysis, a task that can benefit from the emoji calibration technique we use in this work.

Sentiment Analysis

SlangSD: Building and Using a Sentiment Dictionary of Slang Words for Short-Text Sentiment Classification

no code implementations17 Aug 2016 Liang Wu, Fred Morstatter, Huan Liu

To this end, we propose to build the first sentiment dictionary of slang words to aid sentiment analysis of social media content.

General Classification Sentiment Analysis +1

Feature Selection: A Data Perspective

2 code implementations29 Jan 2016 Jundong Li, Kewei Cheng, Suhang Wang, Fred Morstatter, Robert P. Trevino, Jiliang Tang, Huan Liu

To facilitate and promote the research in this community, we also present an open-source feature selection repository that consists of most of the popular feature selection algorithms (\url{http://featureselection. asu. edu/}).

feature selection Sparse Learning

Finding Eyewitness Tweets During Crises

no code implementations WS 2014 Fred Morstatter, Nichola Lubold, Heather Pon-Barry, Jürgen Pfeffer, Huan Liu

These agencies look for tweets from within the region affected by the crisis to get the latest updates of the status of the affected region.

Disaster Response

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