Using style-transfer models to reduce offensiveness of social media comments can help foster a more inclusive environment. However, there are no sizable datasets that contain offensive texts and their inoffensive counterparts, and fine-tuning pretrained models with limited labeled data can lead to the loss of original meaning in the style-transferred text. To address this issue, we provide two major contributions. First, we release the first publicly-available, parallel corpus of offensive Reddit comments and their style-transferred counterparts annotated by expert sociolinguists. Then, we introduce the first discourse-aware style-transfer models that can effectively reduce offensiveness in Reddit text while preserving the meaning of the original text. These models are the first to examine inferential links between the comment and the text it is replying to when transferring the style of offensive Reddit text. We propose two different methods of integrating discourse relations with pretrained transformer models and evaluate them on our dataset of offensive comments from Reddit and their inoffensive counterparts. Improvements over the baseline with respect to both automatic metrics and human evaluation indicate that our discourse-aware models are better at preserving meaning in style-transferred text when compared to the state-of-the-art discourse-agnostic models.

本文描述了通过释义链接迁移Reddit上的冒犯性文本风格的第一个语篇感知风格迁移模型，该模型是通过依靠专家社会语言学家对其进行注释的第一个冒犯性Reddit评论和其无冒犯对应文本的大型数据集训练得出的。经过基于人类评估和自动评估的实验验证，该模型是实验结果中最优秀的一种。

APPDIA：基于Transformer的语篇感知风格转换模型，用于攻击性社交媒体对话