MultiParaDetox: Extending Text Detoxification with Parallel Data to New Languages

• URL: http://arxiv.org/abs/2404.02037v1
• Date: Tue, 2 Apr 2024 15:32:32 GMT
• Title: MultiParaDetox: Extending Text Detoxification with Parallel Data to New Languages
• Authors: Daryna Dementieva, Nikolay Babakov, Alexander Panchenko,
• Abstract summary: Text detoxification is a task where a text is paraphrased from a toxic surface form, e.g. featuring rude words, to the neutral register. Recent approaches for parallel text detoxification corpora collection -- ParaDetox and APPADIA -- were explored only in monolingual setup. In this work, we aim to extend ParaDetox pipeline to multiple languages presenting MultiParaDetox to automate parallel detoxification corpus collection for potentially any language.
• Score: 71.50809576484288
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text detoxification is a textual style transfer (TST) task where a text is paraphrased from a toxic surface form, e.g. featuring rude words, to the neutral register. Recently, text detoxification methods found their applications in various task such as detoxification of Large Language Models (LLMs) (Leong et al., 2023; He et al., 2024; Tang et al., 2023) and toxic speech combating in social networks (Deng et al., 2023; Mun et al., 2023; Agarwal et al., 2023). All these applications are extremely important to ensure safe communication in modern digital worlds. However, the previous approaches for parallel text detoxification corpora collection -- ParaDetox (Logacheva et al., 2022) and APPADIA (Atwell et al., 2022) -- were explored only in monolingual setup. In this work, we aim to extend ParaDetox pipeline to multiple languages presenting MultiParaDetox to automate parallel detoxification corpus collection for potentially any language. Then, we experiment with different text detoxification models -- from unsupervised baselines to LLMs and fine-tuned models on the presented parallel corpora -- showing the great benefit of parallel corpus presence to obtain state-of-the-art text detoxification models for any language.

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