利用情感进行冒犯性文本分类
Leveraging Sentiment for Offensive Text Classification
AI总结：
本文在OLID数据集上利用预训练模型预测情感并训练增强数据集，验证情感信息能提升冒犯性文本分类的整体性能。
摘要：
本文进行实验分析模型能否借助情感更好地分类冒犯性文本。我们在SemEval 2019 task 6，OLID数据集上进行此实验。首先，我们利用预训练语言模型预测每个实例的情感。随后，我们挑选在OLID测试集上取得最佳性能的模型，并在增强的OLID集上训练它以分析性能。结果显示，利用情感提升了模型的整体性能。
英文摘要：
In this paper, we conduct experiment to analyze whether models can classify offensive texts better with the help of sentiment. We conduct this experiment on the SemEval 2019 task 6, OLID, dataset. First, we utilize pre-trained language models to predict the sentiment of each instance. Later we pick the model that achieved the best performance on the OLID test set, and train it on the augmented OLID set to analyze the performance. Results show that utilizing sentiment increases the overall performance of the model.
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。
