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Mitigating Framing Bias with Polarity Minimization Loss: Approach

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This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license.

Authors:

(1) Yejin Bang, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology;

(2) Nayeon Lee, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology;

(3) Pascale Fung, Centre for Artificial Intelligence Research (CAiRE), The Hong Kong University of Science and Technology.

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3. Approach

3.1. Problem Statement

3.2. Polarity Regularized Model



Table 1: Experimental results for ALLSIDES test set. For framing bias metric, the lower number is the better (↓).For other scores, the higher number is the better (↑).

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