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Mitigating Framing Bias with Polarity Minimization Loss: Conclusion
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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.
Table of Links
- Abstract and Intro
- Related Work
- Approach
- Experiments
- Conclusion
- Limitations, Ethics Statement and References
- A. Experimental Details
- B. Generation Results
5. Conclusion
Framing bias is a pervasive problem in modern media, which can lead to a distorted understanding of events and an amplification of polarization. To tackle this, we introduce a polarity minimization loss that reduces framing bias in a generation. Our experimental results demonstrate that incorporating the proposed polarity minimization loss is effective in the reduction of biased uses of language and in removing biased information from the source input, which ultimately mitigates framing bias.
L O A D I N G
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