Papers › Towards Detection of Subjective Bias using Contextualized Word Embeddings
Towards Detection of Subjective Bias using Contextualized Word Embeddings
Tanvi Dadu, Kartikey Pant, Radhika Mamidi
Subjective bias detection is critical for applications like propaganda detection, content recommendation, sentiment analysis, and bias neutralization. This bias is introduced in natural language via inflammatory words and phrases, casting doubt over facts, and presupposing the truth. In this work, we perform comprehensive experiments for detecting subjective bias using BERT-based models on the Wiki Neutrality Corpus(WNC). The dataset consists of $360k$ labeled instances, from Wikipedia edits that remove various instances of the bias. We further propose BERT-based ensembles that outperform state-of-the-art methods like BERT_(large) by a margin of $5.6$ F1 score.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Bias Detection | Wiki Neutrality Corpus | RoBERTa+ALBERT | F1 | 70.4 | #1 of 1 | Archive leaderboard | report |
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