This paper focuses on text detoxification, i.e., automatically converting toxic text into non-toxic text. This task contributes to safer and more respectful online communication and can be considered a Text Style Transfer (TST) task, where the text style changes while its content is preserved. We present three approaches: knowledge transfer from a similar task, multi-task learning approach, combining sequence-to-sequence modeling with various toxicity classification tasks, and, delete and reconstruct approach. To support our research, we utilize a dataset provided by Dementieva et al.(2021), which contains multiple versions of detoxified texts corresponding to toxic texts. In our experiments, we selected the best variants through expert human annotators, creating a dataset where each toxic sentence is paired with a single, appropriate detoxified version. Additionally, we introduced a small Hindi parallel dataset, aligning with a part of the English dataset, suitable for evaluation purposes. Our results demonstrate that our approach effectively balances text detoxication while preserving the actual content and maintaining fluency.

翻译：本文聚焦于文本去毒化任务，即将有毒文本自动转化为无害文本。该任务有助于构建更安全、更文明的在线交流环境，可归类为文本风格迁移（TST）任务——在保持文本内容不变的同时改变其风格。我们提出了三种方法：基于相似任务的知识迁移、结合序列到序列建模与多种毒性分类任务的多任务学习方法，以及删除与重构方法。为支持研究，我们使用了Dementieva等人（2021）提供的包含多版去毒化文本对应有毒文本的数据集。实验中，我们通过专家人工标注筛选出最优版本，构建了每个有毒句子仅对应单一恰当去毒化版本的数据集。此外，我们还引入了一个小型印地语平行数据集，其内容与部分英语数据集对齐，适用于评估目的。实验结果表明，我们的方法能够有效平衡文本去毒化效果，同时保留原始内容并保持语言流畅性。