  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.
