一种利用基于用户信息的统一多任务学习架构用于仇恨检测
A Unified Multi-Task Learning Architecture for Hate Detection Leveraging User-Based Information
AI总结：
本文提出一种统一的多任务学习架构，通过结合用户内部与用户间信息及文本特征进行英语仇恨言论检测，在三个基准数据集上的实验表明该方法显著提升了macro-F1和weighted-F1指标。
摘要：
仇恨言论、攻击性语言、攻击行为、种族主义、性别歧视及其他辱骂性语言是社交媒体中的常见现象。需要基于人工智能（AI）的干预措施，以大规模过滤仇恨内容。大多数现有的仇恨言论检测解决方案将每个帖子视为分类的孤立输入实例来利用特征。本文通过引入一个独特的模型来解决这一问题，该模型通过利用用户内部和用户间的信息来改进英语的仇恨言论识别。实验在单任务学习（STL）和多任务学习（MTL）范式下进行，使用了深度神经网络，如卷积神经网络（CNN）、门控循环单元（GRU）、来自transformer的双向编码器表示（BERT）和轻量级BERT（ALBERT）。我们使用了三个基准数据集，并得出结论：将某些用户特征与文本特征相结合，在macro-F1和weighted-F1上带来了显著提升。
英文摘要：
Hate speech, offensive language, aggression, racism, sexism, and other abusive language are common phenomena in social media. There is a need for Artificial Intelligence(AI)based intervention which can filter hate content at scale. Most existing hate speech detection solutions have utilized the features by treating each post as an isolated input instance for the classification. This paper addresses this issue by introducing a unique model that improves hate speech identification for the English language by utilising intra-user and inter-user-based information. The experiment is conducted over single-task learning (STL) and multi-task learning (MTL) paradigms that use deep neural networks, such as convolutional neural networks (CNN), gated recurrent unit (GRU), bidirectional encoder representations from the transformer (BERT), and A Lite BERT (ALBERT). We use three benchmark datasets and conclude that combining certain user features with textual features gives significant improvements in macro-F1 and weighted-F1.
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。
