论文标题
冷：中国进攻性语言检测的基准
COLD: A Benchmark for Chinese Offensive Language Detection
论文作者
论文摘要
进攻性语言检测对于维持文明的社交媒体平台和部署预训练的语言模型至关重要。但是，由于可靠的数据集缺乏，中文中的这项任务仍在勘探中。为此，我们提出了一个基准 - 中国进攻性语言分析的基准，包括中国进攻性语言数据集（coldataset）和基线检测器（基线检测器） - 对数据集进行了培训。我们表明，冷基准有助于中国进攻性语言检测，这对现有资源充满挑战。然后，我们部署了冷漠者，并对中国流行的预训练语言模型进行了详细分析。我们首先分析了现有生成模型的进攻性，并表明这些模型不可避免地暴露了不同程度的进攻问题。此外，我们研究了影响进攻世代的因素，我们发现反偏见的内容和关键字指的是某些群体或揭示负面态度会引发进攻性输出更容易。
Offensive language detection is increasingly crucial for maintaining a civilized social media platform and deploying pre-trained language models. However, this task in Chinese is still under exploration due to the scarcity of reliable datasets. To this end, we propose a benchmark --COLD for Chinese offensive language analysis, including a Chinese Offensive Language Dataset --COLDATASET and a baseline detector --COLDETECTOR which is trained on the dataset. We show that the COLD benchmark contributes to Chinese offensive language detection which is challenging for existing resources. We then deploy the COLDETECTOR and conduct detailed analyses on popular Chinese pre-trained language models. We first analyze the offensiveness of existing generative models and show that these models inevitably expose varying degrees of offensive issues. Furthermore, we investigate the factors that influence the offensive generations, and we find that anti-bias contents and keywords referring to certain groups or revealing negative attitudes trigger offensive outputs easier.
