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Using style-transfer models to reduce offensiveness of social media commentscan help foster a more inclusive environment. However, there are no sizabledatasets that contain offensive texts and their inoffensive counterparts, andfine-tuning pretrained models with limited labeled data can lead to the loss oforiginal meaning in the style-transferred text. To address this issue, weprovide two major contributions. First, we release the firstpublicly-available, parallel corpus of offensive Reddit comments and theirstyle-transferred counterparts annotated by expert sociolinguists. Then, weintroduce the first discourse-aware style-transfer models that can effectivelyreduce offensiveness in Reddit text while preserving the meaning of theoriginal text. These models are the first to examine inferential links betweenthe comment and the text it is replying to when transferring the style ofoffensive Reddit text. We propose two different methods of integratingdiscourse relations with pretrained transformer models and evaluate them on ourdataset of offensive comments from Reddit and their inoffensive counterparts.Improvements over the baseline with respect to both automatic metrics and humanevaluation indicate that our discourse-aware models are better at preservingmeaning in style-transferred text when compared to the state-of-the-artdiscourse-agnostic models.

APPDIA: A Discourse-aware Transformer-based Style Transfer Model for Offensive Social Media Conversations