Multilingual and Explainable Text Detoxification with Parallel Corpora
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916525755072512 |
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| author | Dementieva, Daryna Babakov, Nikolay Ronen, Amit Ayele, Abinew Ali Rizwan, Naquee Schneider, Florian Wang, Xintong Yimam, Seid Muhie Moskovskiy, Daniil Stakovskii, Elisei Kaufman, Eran Elnagar, Ashraf Mukherjee, Animesh Panchenko, Alexander |
| author_facet | Dementieva, Daryna Babakov, Nikolay Ronen, Amit Ayele, Abinew Ali Rizwan, Naquee Schneider, Florian Wang, Xintong Yimam, Seid Muhie Moskovskiy, Daniil Stakovskii, Elisei Kaufman, Eran Elnagar, Ashraf Mukherjee, Animesh Panchenko, Alexander |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_11691 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multilingual and Explainable Text Detoxification with Parallel Corpora Dementieva, Daryna Babakov, Nikolay Ronen, Amit Ayele, Abinew Ali Rizwan, Naquee Schneider, Florian Wang, Xintong Yimam, Seid Muhie Moskovskiy, Daniil Stakovskii, Elisei Kaufman, Eran Elnagar, Ashraf Mukherjee, Animesh Panchenko, Alexander Computation and Language Artificial Intelligence 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. |
| title | Multilingual and Explainable Text Detoxification with Parallel Corpora |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2412.11691 |