Text Detoxification as Style Transfer in English and Hindi

• URL: http://arxiv.org/abs/2402.07767v2
• Date: Sun, 9 Jun 2024 18:48:06 GMT
• Title: Text Detoxification as Style Transfer in English and Hindi
• Authors: Sourabrata Mukherjee, Akanksha Bansal, Atul Kr. Ojha, John P. McCrae, Ondřej Dušek,
• Abstract summary: This paper focuses on text detoxification, i.e., automatically converting toxic text into non-toxic text. We present three approaches: knowledge transfer from a similar task, multi-task learning approach, and delete and reconstruct approach. Our results demonstrate that our approach effectively balances text detoxication while preserving the actual content and maintaining fluency.
• Score: 1.183205689022649
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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