用情感信息提升仇恨文本分类效果
Leveraging Sentiment for Offensive Text Classification
- 用预训练模型预测文本情感，辅助分类任务
- 在OLID数据集上准确率提升，整体性能更好
- 适合关注社交媒体内容安全的研究者
本文研究情感信息是否有助于提升仇恨文本分类性能。实验基于SemEval 2019任务6的OLID数据集，首先利用预训练语言模型对每条文本进行情感预测，随后选取在测试集表现最佳的模型，在增强版OLID数据集上重新训练并评估性能。结果表明，引入情感信息可有效提升模型整体分类效果。
原文摘要 · Abstract (English)
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.
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