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Found 10 papers, 5 papers with code
Subjective Crowd Disagreements for Subjective Data: Uncovering Meaningful CrowdOpinion with Population-level Learning
Human-annotated data plays a critical role in the fairness of AI systems, including those that deal with life-altering decisions or moderating human-created web/social media content.
Vicarious Offense and Noise Audit of Offensive Speech Classifiers: Unifying Human and Machine Disagreement on What is Offensive
For (2), we introduce a first-of-its-kind dataset of vicarious offense.
Cross-lingual Offensive Language Identification for Low Resource Languages: The Case of Marathi
The widespread presence of offensive language on social media motivated the development of systems capable of recognizing such content automatically.
Improving Label Quality by Jointly Modeling Items and Annotators
We propose a fully Bayesian framework for learning ground truth labels from noisy annotators.
LCP-RIT at SemEval-2021 Task 1: Exploring Linguistic Features for Lexical Complexity Prediction
This paper describes team LCP-RIT's submission to the SemEval-2021 Task 1: Lexical Complexity Prediction (LCP).
Domain-specific MT for Low-resource Languages: The case of Bambara-French
Translating to and from low-resource languages is a challenge for machine translation (MT) systems due to a lack of parallel data.
Assessing Human Translations from French to Bambara for Machine Learning: a Pilot Study
We present novel methods for assessing the quality of human-translated aligned texts for learning machine translation models of under-resourced languages.
Neighborhood-based Pooling for Population-level Label Distribution Learning
Supervised machine learning often requires human-annotated data.
Twitter Job/Employment Corpus: A Dataset of Job-Related Discourse Built with Humans in the Loop
We present the Twitter Job/Employment Corpus, a collection of tweets annotated by a humans-in-the-loop supervised learning framework that integrates crowdsourcing contributions and expertise on the local community and employment environment.
Learning from various labeling strategies for suicide-related messages on social media: An experimental study
Suicide is an important but often misunderstood problem, one that researchers are now seeking to better understand through social media.
