2.4k citations · 2.7k across the 42 of their papers we have counts for
15 papers · 1 filter
Don't Blame the Data, Blame the Model: Understanding Noise and Bias When Learning from Subjective Annotations
Abhishek Anand, Negar Mokhberian, Prathyusha Naresh Kumar +5
Researchers have raised awareness about the harms of aggregating labels especially in subjective tasks that naturally contain disagreements among human annotators. In this work we…
Capturing Perspectives of Crowdsourced Annotators in Subjective Learning Tasks
Negar Mokhberian, Myrl G. Marmarelis, Frederic R. Hopp +3
Supervised classification heavily depends on datasets annotated by humans. However, in subjective tasks such as toxicity classification, these annotations often exhibit low agreeme…
The Unequal Opportunities of Large Language Models: Revealing Demographic Bias through Job Recommendations
Abel Salinas, Parth Vipul Shah, Yuzhong Huang +2
Large Language Models (LLMs) have seen widespread deployment in various real-world applications. Understanding these biases is crucial to comprehend the potential downstream conseq…
Modeling Cross-Cultural Pragmatic Inference with Codenames Duet
Omar Shaikh, Caleb Ziems, William Held +3
Pragmatic reference enables efficient interpersonal communication. Prior work uses simple reference games to test models of pragmatic reasoning, often with unidentified speakers an…
Contextualizing Argument Quality Assessment with Relevant Knowledge
Darshan Deshpande, Zhivar Sourati, Filip Ilievski +1
Automatic assessment of the quality of arguments has been recognized as a challenging task with significant implications for misinformation and targeted speech. While real-world ar…
Temporal Knowledge Graph Forecasting Without Knowledge Using In-Context Learning
Dong-Ho Lee, Kian Ahrabian, Woojeong Jin +2
Temporal knowledge graph (TKG) forecasting benchmarks challenge models to predict future facts using knowledge of past facts. In this paper, we apply large language models (LLMs) t…
Noise Audits Improve Moral Foundation Classification
Negar Mokhberian, Frederic R. Hopp, Bahareh Harandizadeh +2
Morality plays an important role in culture, identity, and emotion. Recent advances in natural language processing have shown that it is possible to classify moral values expressed…
Robust Conversational Agents against Imperceptible Toxicity Triggers
Ninareh Mehrabi, Ahmad Beirami, Fred Morstatter +1
Warning: this paper contains content that maybe offensive or upsetting. Recent research in Natural Language Processing (NLP) has advanced the development of various toxicity detect…
"Stop Asian Hate!" : Refining Detection of Anti-Asian Hate Speech During the COVID-19 Pandemic
Huy Nghiem, Fred Morstatter
Content warning: This work displays examples of explicit and/or strongly offensive language. Fueled by a surge of anti-Asian xenophobia and prejudice during the COVID-19 pandemic,…
AutoTriggER: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction
Dong-Ho Lee, Ravi Kiran Selvam, Sheikh Muhammad Sarwar +6
Deep neural models for named entity recognition (NER) have shown impressive results in overcoming label scarcity and generalizing to unseen entities by leveraging distant supervisi…
Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources
Ninareh Mehrabi, Pei Zhou, Fred Morstatter +3
Warning: this paper contains content that may be offensive or upsetting. Numerous natural language processing models have tried injecting commonsense by using the ConceptNet knowle…
Identifying Distributional Perspective Differences from Colingual Groups
Yufei Tian, Tuhin Chakrabarty, Fred Morstatter +1
Perspective differences exist among different cultures or languages. A lack of mutual understanding among different groups about their perspectives on specific values or events may…
Cross-Platform Emoji Interpretation: Analysis, a Solution, and Applications
Fred Morstatter, Kai Shu, Suhang Wang +1
Most social media platforms are largely based on text, and users often write posts to describe where they are, what they are seeing, and how they are feeling. Because written text…
SlangSD: Building and Using a Sentiment Dictionary of Slang Words for Short-Text Sentiment Classification
Liang Wu, Fred Morstatter, Huan Liu
Sentiment in social media is increasingly considered as an important resource for customer segmentation, market understanding, and tackling other socio-economic issues. However, se…
Finding Eyewitness Tweets During Crises
Fred Morstatter, Nichola Lubold, Heather Pon-Barry +2
Disaster response agencies have started to incorporate social media as a source of fast-breaking information to understand the needs of people affected by the many crises that occu…