Pseudo-labelling Enhanced Media Bias Detection
This work addresses media bias detection, which is important for researchers and practitioners in NLP, but it appears incremental as it builds on existing pseudo-labelling and distant supervision techniques.
The paper tackled the problem of detecting media bias in text by proposing a pseudo-labelling method to select samples from noisy distant supervision datasets, resulting in improved accuracy for biased news detection models.
Leveraging unlabelled data through weak or distant supervision is a compelling approach to developing more effective text classification models. This paper proposes a simple but effective data augmentation method, which leverages the idea of pseudo-labelling to select samples from noisy distant supervision annotation datasets. The result shows that the proposed method improves the accuracy of biased news detection models.