Controllable debiasing is a new formulation of stylistic rewriting that aims to rewrite a given text and correct the implicit and potentially undesirable biases in character portrayals.
In our work, we analyze gender bias in portrayal through the lens of connotation frames of power and agency, which capture knowledge about the implied power dynamics with respect to verbs. In our previous work, we showed that authors attribute significantly less power and less agency to female characters compared to male characters. Therefore, here, we create
Our model is an encoder-decoder based on a pretrained language model. We train it to reconstruct story sentences from which we've remove agency markers (i.e., verbs). Additionally, we also jointly train it on an out-of-domain paraphrasing task, which teaches the model to rewrite more than just one word. Then, at test time, we also incorporate agency information by boosting the probability of words with the desired level of agency. In our paper, we explore how important different components of this model are, and show that both the joint reconstruction-paraphrasing and the vocab boosting yield significant benefits in performance.
As a case study for our model, we re-visit the movie scripts from our original analyses and attempt to rewrite the sentences that describe female characters to give them higher agency.
We show that, with
We believe that this task has the potential to help authors when writing stories or movies, by providing alternative portrayals of characters with different connotations or framings.
Specifically, a machine-in-the-loop writing system could help authors measure and address biases in their writing using
MTurk templates: [Agency qualification task] [Head-to-head evaluation]
Xinyao (Michelle) Ma, Maarten Sap, Hannah Rashkin & Yejin Choi (2020).
PowerTransformer: Unsupervised Controllable Revision for Biased Language Correction. EMNLP
@inproceedings{ma2020powertransformer, title={PowerTransformer: Unsupervised Controllable Revision for Biased Language Correction}, author={Ma, Xinyao and Sap, Maarten and Rashkin, Hannah and Choi, Yejin}, year={2020}, booktitle={EMNLP}, }