Exploring Methods for Cross-lingual Text Style Transfer: The Case of Text Detoxification
Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)(2023)
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.
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