西语和约鲁巴语中的文本净化：一种跨语言机器学习方法用于资源有限的非洲语言
Text Detoxification in isiXhosa and Yorùbá: A Cross-Lingual Machine Learning Approach for Low-Resource African Languages
AI总结：
本研究提出了一种跨语言机器学习方法，用于西语和约鲁巴语的文本净化，结合可解释模型和规则编辑，实现了高准确率的有毒文本检测与重写。
摘要：
有毒语言是安全在线参与的主要障碍之一，但针对非洲语言的稳健缓解工具却十分稀缺。本研究通过研究两种低资源非洲语言（西语和约鲁巴语）的自动文本净化（将有毒文本转换为中性文本）来解决这一关键缺口。这项工作贡献了一种新颖的、务实的混合方法：一种轻量级、可解释的TF-IDF和逻辑回归模型用于透明的毒性检测，以及一个受控的词典和令牌引导的重写组件。为了训练和评估模型，开发了一个包含有毒到中性重写的平行语料库，该语料库捕捉了习语用法、变音符号和代码切换。检测组件在分层K折验证中的准确率达到了61-72%（西语）和72-86%（约鲁巴语），每种语言的ROC-AUC最高达到0.88。重写组件成功地净化了所有检测到的有毒句子，同时保持了100%的非有毒句子。这些结果表明，可扩展、可解释的机器学习检测器结合规则编辑提供了一种具有竞争力且资源高效的解决方案，为文化适应的安全工具设定了一个新的基准，为非洲语言中的低资源文本风格转换（TST）树立了新标准。
英文摘要：
Toxic language is one of the major barrier to safe online participation, yet robust mitigation tools are scarce for African languages. This study addresses this critical gap by investigating automatic text detoxification (toxic to neutral rewriting) for two low-resource African languages, isiXhosa and Yorùbá. The work contributes a novel, pragmatic hybrid methodology: a lightweight, interpretable TF-IDF and Logistic Regression model for transparent toxicity detection, and a controlled lexicon- and token-guided rewriting component. A parallel corpus of toxic to neutral rewrites, which captures idiomatic usage, diacritics, and code switching, was developed to train and evaluate the model. The detection component achieved stratified K-fold accuracies of 61-72% (isiXhosa) and 72-86% (Yorùbá), with per-language ROC-AUCs up to 0.88. The rewriting component successfully detoxified all detected toxic sentences while preserving 100% of non-toxic sentences. These results demonstrate that scalable, interpretable machine learning detectors combined with rule-based edits offer a competitive and resource-efficient solution for culturally adaptive safety tooling, setting a new benchmark for low-resource Text Style Transfer (TST) in African 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。
