Learning to Flip the Bias of News Headlines

Wei-Fan Chen, Henning Wachsmuth, Khalid Al-Khatib, Benno Stein

Abstract
This paper introduces the task of “flipping” the bias of news articles: Given an article with a political bias (left or right), generate an article with the same topic but opposite bias. To study this task, we create a corpus with bias-labeled articles from all-sides.com. As a first step, we analyze the corpus and discuss intrinsic characteristics of bias. They point to the main challenges of bias flipping, which in turn lead to a specific setting in the generation process. The paper in hand narrows down the general bias flipping task to focus on bias flipping for news article headlines. A manual annotation of headlines from each side reveals that they are self-informative in general and often convey bias. We apply an autoencoder incorporating information from an article’s content to learn how to automatically flip the bias. From 200 generated headlines, 73 are classified as understandable by annotators, and 83 maintain the topic while having opposite bias. Insights from our analysis shed light on how to solve the main challenges of bias flipping.
Anthology ID:
W18-6509
Volume:
Proceedings of the 11th International Conference on Natural Language Generation
Month:
November
Year:
2018
Address:
Tilburg University, The Netherlands
Venue:
INLG
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
79–88
Language:
URL:
https://aclanthology.org/W18-6509
DOI:
10.18653/v1/W18-6509
Bibkey:
Cite (ACL):
Wei-Fan Chen, Henning Wachsmuth, Khalid Al-Khatib, and Benno Stein. 2018. Learning to Flip the Bias of News Headlines. In Proceedings of the 11th International Conference on Natural Language Generation, pages 79–88, Tilburg University, The Netherlands. Association for Computational Linguistics.
Cite (Informal):
Learning to Flip the Bias of News Headlines (Chen et al., INLG 2018)
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PDF:
https://preview.aclanthology.org/paclic-22-ingestion/W18-6509.pdf
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