论文标题
没有像现在这样的时间：语言变化对自动评论的影响
No Time Like the Present: Effects of Language Change on Automated Comment Moderation
论文作者
论文摘要
对于主持评论部分的报纸来说，在线仇恨的传播已成为一个重大问题。结果，对（半）自动化语言检测使用机器学习和自然语言处理的兴趣越来越大，以避免手动评论审核成本或必须完全关闭评论部分。但是，尽管语言和新闻一直处于不断变化状态，但过去的许多关于滥用语言检测的工作都假定分类器在静态语言环境中运行。在本文中，我们使用新的德国报纸评论数据集表明，接受NAIVE ML技术（如随机测试列车拆分）训练的分类器在未来的数据上表现不佳，并且时间分层的评估分配更合适。我们还表明，分类器的性能在与培训数据不同时期的数据中评估时会迅速降低。我们的发现表明，在开发滥用语言检测系统或风险部署模型时，有必要考虑语言的时间动态，该模型将很快被删除。
The spread of online hate has become a significant problem for newspapers that host comment sections. As a result, there is growing interest in using machine learning and natural language processing for (semi-) automated abusive language detection to avoid manual comment moderation costs or having to shut down comment sections altogether. However, much of the past work on abusive language detection assumes that classifiers operate in a static language environment, despite language and news being in a state of constant flux. In this paper, we show using a new German newspaper comments dataset that the classifiers trained with naive ML techniques like a random-test train split will underperform on future data, and that a time stratified evaluation split is more appropriate. We also show that classifier performance rapidly degrades when evaluated on data from a different period than the training data. Our findings suggest that it is necessary to consider the temporal dynamics of language when developing an abusive language detection system or risk deploying a model that will quickly become defunct.
