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Unconscious biases continue to be prevalent in modern text and media, callingfor algorithms that can assist writers with bias correction. For example, afemale character in a story is often portrayed as passive and powerless ("Shedaydreams about being a doctor") while a man is portrayed as more proactive andpowerful ("He pursues his dream of being a doctor").  We formulate *Controllable Debiasing*, a new revision task that aims torewrite a given text to correct the implicit and potentially undesirable biasin character portrayals. We then introduce PowerTransformer as an approach thatdebiases text through the lens of connotation frames (Sap et al., 2017), whichencode pragmatic knowledge of implied power dynamics with respect to verbpredicates. One key challenge of our task is the lack of parallel corpora. Toaddress this challenge, we adopt an unsupervised approach using auxiliarysupervision with related tasks such as paraphrasing and self-supervision basedon a reconstruction loss, building on pretrained language models.  Through comprehensive experiments based on automatic and human evaluations,we demonstrate that our approach outperforms ablations and existing methodsfrom related tasks. Furthermore, we demonstrate the use of PowerTransformer asa step toward mitigating the well-documented gender bias in character portrayalin movie scripts.

PowerTransformer: Unsupervised Controllable Revision for Biased Language Correction