论文标题
利用情感双向变压器进行进攻性语言检测
Leveraging Affective Bidirectional Transformers for Offensive Language Detection
论文作者
论文摘要
社交媒体在我们的生活中无处不在，因此有必要通过检测和消除冒犯性和仇恨言论来确保安全的在线体验。在这项工作中，我们将提交的意见报告与第四届开源阿拉伯语料库和加工工具阿拉伯语（OSACT4）的第四届研讨会组织的仇恨语言检测共享任务。我们专注于开发纯粹的深度学习系统，而无需功能工程。为此，我们开发了一种自动数据增强的有效方法，并显示了培训进攻性和仇恨言论模型（即通过微调）先前训练的情感模型（即情感和情感）的实用性。我们的最佳模型要比官方测试数据的仇恨言论（82.31％宏F1）的香草BERT模型要好得多，仇恨言论为89.60％ACC（82.31％宏观F1）和95.20％的ACC（70.51％宏F1）。
Social media are pervasive in our life, making it necessary to ensure safe online experiences by detecting and removing offensive and hate speech. In this work, we report our submission to the Offensive Language and hate-speech Detection shared task organized with the 4th Workshop on Open-Source Arabic Corpora and Processing Tools Arabic (OSACT4). We focus on developing purely deep learning systems, without a need for feature engineering. For that purpose, we develop an effective method for automatic data augmentation and show the utility of training both offensive and hate speech models off (i.e., by fine-tuning) previously trained affective models (i.e., sentiment and emotion). Our best models are significantly better than a vanilla BERT model, with 89.60% acc (82.31% macro F1) for hate speech and 95.20% acc (70.51% macro F1) on official TEST data.
