AustroTox：一个用于基于目标的奥地利德语攻击性语言检测的数据集
AustroTox: A Dataset for Target-Based Austrian German Offensive Language Detection
AI总结：
本文提出AustroTox数据集，包含4,562条奥地利德语新闻论坛评论，标注攻击性及粗俗语言和目标片段，并比较微调模型与大型语言模型在攻击性检测上的表现。
摘要：
毒性检测中的模型可解释性极大地受益于词元级标注。然而，目前此类标注仅适用于英语。我们引入了一个来自新闻论坛的、用于攻击性语言检测的标注数据集，其显著特点是纳入了奥地利德语方言，包含4,562条用户评论。除了二元的攻击性分类之外，我们还识别每条评论中构成粗俗语言或代表攻击性陈述目标的片段。我们以微调语言模型以及零样本和少样本方式评估大型语言模型。结果表明，虽然微调模型在检测粗俗方言等语言特殊性方面表现出色，但大型语言模型在检测AustroTox中的攻击性方面表现出更优越的性能。我们公开了数据和代码。
英文摘要：
Model interpretability in toxicity detection greatly profits from token-level annotations. However, currently such annotations are only available in English. We introduce a dataset annotated for offensive language detection sourced from a news forum, notable for its incorporation of the Austrian German dialect, comprising 4,562 user comments. In addition to binary offensiveness classification, we identify spans within each comment constituting vulgar language or representing targets of offensive statements. We evaluate fine-tuned language models as well as large language models in a zero- and few-shot fashion. The results indicate that while fine-tuned models excel in detecting linguistic peculiarities such as vulgar dialect, large language models demonstrate superior performance in detecting offensiveness in AustroTox. We publish the data and code.
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。
