2025
EMNLP
EMNLP 2025
McMaster at LeWiDi-2025: Demographic-Aware RoBERTa
Abstract
AbstractWe present our submission to the Learning With Disagreements (LeWiDi) 2025 shared task. Our team implemented a variety of BERT-based models that encode annotator meta-data in combination with text to predict soft-label distributions and individual annotator labels. We show across four tasks that a combination of demographic factors leads to improved performance, however through ablations across all demographic variables we find that in some cases, a single variable performs best. Our approach placed 4th in the overall competition.
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Interdisciplinary Bridge
— Computer Science and Data Science & Analytics and Deep Learning and Machine Learning
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Keyword Pioneer
— soft-label prediction
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio