探索跨语言文本风格迁移的方法：以文本去毒为例
Exploring Methods for Cross-lingual Text Style Transfer: The Case of Text Detoxification
AI总结：
本研究大规模探索跨语言文本去毒策略，首次提出同时进行文本翻译与去毒的新任务并提供强基线，同时引入与人类判断高度相关的自动评估指标，最终通过人工标注确定了最佳跨语言知识迁移策略。
摘要：
文本去毒是将文本风格从有毒转换为中立的任务。尽管在单语设置下已有方法取得了有前景的结果，例如（Dale et al., 2021; Hallinan et al., 2022），但该任务的跨语言迁移仍是一个具有挑战性的开放问题（Moskovskiy et al., 2022）。在本研究中，我们对跨语言文本去毒的策略进行了大规模研究——给定一种语言的平行去毒语料库；目标是将去毒能力迁移到我们没有此类语料库的另一种语言。此外，我们是首个探索同时进行文本翻译和去毒的新任务，并为该任务提供了几个强基线。最后，我们引入了新的自动去毒评估指标，其与人类判断的相关性高于先前的基准。我们还通过人工标注评估了最有前景的方法，确定了在语言之间迁移文本去毒知识的最佳策略。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
