Multilingual and Explainable Text Detoxification with Parallel Corpora

• URL: http://arxiv.org/abs/2412.11691v1
• Date: Mon, 16 Dec 2024 12:08:59 GMT
• Title: Multilingual and Explainable Text Detoxification with Parallel Corpora
• Authors: Daryna Dementieva, Nikolay Babakov, Amit Ronen, Abinew Ali Ayele, Naquee Rizwan, Florian Schneider, Xintong Wang, Seid Muhie Yimam, Daniil Moskovskiy, Elisei Stakovskii, Eran Kaufman, Ashraf Elnagar, Animesh Mukherjee, Alexander Panchenko,
• Abstract summary: We extend parallel text detoxification corpus to new languages. We conduct the first of its kind an automated, explainable analysis of the descriptive features of both toxic and non-toxic sentences. We then experiment with a novel text detoxification method inspired by the Chain-of-Thoughts reasoning approach.
• Score: 58.83211571400692
• License:
• Abstract: Even with various regulations in place across countries and social media platforms (Government of India, 2021; European Parliament and Council of the European Union, 2022, digital abusive speech remains a significant issue. One potential approach to address this challenge is automatic text detoxification, a text style transfer (TST) approach that transforms toxic language into a more neutral or non-toxic form. To date, the availability of parallel corpora for the text detoxification task (Logachevavet al., 2022; Atwell et al., 2022; Dementievavet al., 2024a) has proven to be crucial for state-of-the-art approaches. With this work, we extend parallel text detoxification corpus to new languages -- German, Chinese, Arabic, Hindi, and Amharic -- testing in the extensive multilingual setup TST baselines. Next, we conduct the first of its kind an automated, explainable analysis of the descriptive features of both toxic and non-toxic sentences, diving deeply into the nuances, similarities, and differences of toxicity and detoxification across 9 languages. Finally, based on the obtained insights, we experiment with a novel text detoxification method inspired by the Chain-of-Thoughts reasoning approach, enhancing the prompting process through clustering on relevant descriptive attributes.

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