2024
EMNLP
EMNLP 2024
WikiBias as an Extrapolation Corpus for Bias Detection
Abstract
AbstractThis paper explores whether it is possible to train a machine learning model using Wikipedia data to detect subjectivity in sentences and generalize effectively to other domains. To achieve this, we performed experiments with the WikiBias corpus, the BABE corpus, and the CheckThat! Dataset. Various classical models for ML were tested, including Logistic Regression, SVC, and SVR, including characteristics such as Sentence Transformers similarity, probabilistic sentiment measures, and biased lexicons. Pre-trained models like DistilRoBERTa, as well as large language models like Gemma and GPT-4, were also tested for the same classification task.
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning and Natural Language Processing
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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