71 citations · 158 across the 12 of their papers we have counts for
13 papers
How Crowd Worker Factors Influence Subjective Annotations: A Study of Tagging Misogynistic Hate Speech in Tweets
Danula Hettiachchi, Indigo Holcombe-James, Stephanie Livingstone +4
Crowdsourced annotation is vital to both collecting labelled data to train and test automated content moderation systems and to support human-in-the-loop review of system decisions…
Designing Closed-Loop Models for Task Allocation
Vijay Keswani, L. Elisa Celis, Krishnaram Kenthapadi +1
Automatically assigning tasks to people is challenging because human performance can vary across tasks for many reasons. This challenge is further compounded in real-life settings…
Same Same, But Different: Conditional Multi-Task Learning for Demographic-Specific Toxicity Detection
Soumyajit Gupta, Sooyong Lee, Maria De-Arteaga +1
Algorithmic bias often arises as a result of differential subgroup validity, in which predictive relationships vary across groups. For example, in toxic language detection, comment…
New Metrics to Encourage Innovation and Diversity in Information Retrieval Approaches
Mehmet Deniz Türkmen, Matthew Lease, Mucahid Kutlu
In evaluation campaigns, participants often explore variations of popular, state-of-the-art baselines as a low-risk strategy to achieve competitive results. While effective, this c…
The State of Human-centered NLP Technology for Fact-checking
Anubrata Das, Houjiang Liu, Venelin Kovatchev +1
Misinformation threatens modern society by promoting distrust in science, changing narratives in public health, heightening social polarization, and disrupting democratic elections…
longhorns at DADC 2022: How many linguists does it take to fool a Question Answering model? A systematic approach to adversarial attacks
Venelin Kovatchev, Trina Chatterjee, Venkata S Govindarajan +9
Developing methods to adversarially challenge NLP systems is a promising avenue for improving both model performance and interpretability. Here, we describe the approach of the tea…