Text Detoxification as Style Transfer in English and Hindi
Sourabrata Mukherjee Akanksha Bansal Atul Kr. Ojha John P. McCrae Ondřej Dušek
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.