Text detoxification is the task of transferring the style of text from toxic to neutral. While here are approaches yielding promising results in monolingual setup, e.g., (Dale et al., 2021; Hallinan et al., 2022), cross-lingual transfer for this task remains a challenging open problem (Moskovskiy et al., 2022). In this work, we present a large-scale study of strategies for cross-lingual text detoxification -- given a parallel detoxification corpus for one language; the goal is to transfer detoxification ability to another language for which we do not have such a corpus. Moreover, we are the first to explore a new task where text translation and detoxification are performed simultaneously, providing several strong baselines for this task. Finally, we introduce new automatic detoxification evaluation metrics with higher correlations with human judgments than previous benchmarks. We assess the most promising approaches also with manual markup, determining the answer for the best strategy to transfer the knowledge of text detoxification between languages.

文本去毒化是将文本风格从有毒转化为中性的任务，在单语环境下已有取得良好结果的方法，但在该任务的跨语言转化中仍然存在困难。本工作首次探索了一种新任务，即同时进行文本翻译和去毒化，为该任务提供了几个强基线方法。此外，引入了与人类判断更高相关性的新自动去毒化评估指标，通过人工标记和评估最有希望的方法，确定了文本去毒化知识转移的最佳策略。

跨语言文本风格转换方法研究：以文本排毒为例