论文标题
Duluth在Semeval-2020任务12：以逻辑回归为英文的进攻性推文身份
Duluth at SemEval-2020 Task 12: Offensive Tweet Identification in English with Logistic Regression
论文作者
论文摘要
本文介绍了参与Semeval-2020任务12的Duluth系统，社交媒体中的多语言攻击语言识别（Insenseval-2020）。我们参加了三种英语任务。我们的系统使用逻辑回归提供了简单的机器学习基线。我们对任务组织者提供的遥远监督培训数据培训了模型，并且没有使用其他资源。可以预料的是，我们在比较评估中没有高度排名：任务A的第85位，第34位，任务B中的第43位，在任务C中的第39位中的第24位。我们对我们的结果进行了定性分析，发现黄金标准数据中的类标签有些嘈杂。我们假设，最高排名的系统的极高准确度（> 90％）可能反映出可以很好地学习培训数据但可能无法推广到以英语识别进攻性语言的任务。该分析包括推文的示例，尽管被轻度编辑仍然是令人反感的。
This paper describes the Duluth systems that participated in SemEval--2020 Task 12, Multilingual Offensive Language Identification in Social Media (OffensEval--2020). We participated in the three English language tasks. Our systems provide a simple Machine Learning baseline using logistic regression. We trained our models on the distantly supervised training data made available by the task organizers and used no other resources. As might be expected we did not rank highly in the comparative evaluation: 79th of 85 in Task A, 34th of 43 in Task B, and 24th of 39 in Task C. We carried out a qualitative analysis of our results and found that the class labels in the gold standard data are somewhat noisy. We hypothesize that the extremely high accuracy (> 90%) of the top ranked systems may reflect methods that learn the training data very well but may not generalize to the task of identifying offensive language in English. This analysis includes examples of tweets that despite being mildly redacted are still offensive.
