  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.
