以英语和印阿里亚语言发表的仇恨言论和攻击性内容识别 (Overview of the HASOC Subtrack at FIRE 2021: Hate Speech and Offensive Content Identification in English and Indo-Aryan Languages)

Thomas Mandl,Sandip Modha,Gautam Kishore Shahi,Hiren Madhu,Shrey Satapara,Prasenjit Majumder,Johannes Schaefer,Tharindu Ranasinghe,Marcos Zampieri,Durgesh Nandini,Amit Kumar Jaiswal

The widespread of offensive content online such as hate speech poses a growing societal problem. AI tools are necessary for supporting the moderation process at online platforms. For the evaluation of these identification tools, continuous experimentation with data sets in different languages are necessary. The HASOC track (Hate Speech and Offensive Content Identification) is dedicated to develop benchmark data for this purpose. This paper presents the HASOC subtrack for English, Hindi, and Marathi. The data set was assembled from Twitter. This subtrack has two sub-tasks. Task A is a binary classification problem (Hate and Not Offensive) offered for all three languages. Task B is a fine-grained classification problem for three classes (HATE) Hate speech, OFFENSIVE and PROFANITY offered for English and Hindi. Overall, 652 runs were submitted by 65 teams. The performance of the best classification algorithms for task A are F1 measures 0.91, 0.78 and 0.83 for Marathi, Hindi and English, respectively. This overview presents the tasks and the data development as well as the detailed results. The systems submitted to the competition applied a variety of technologies. The best performing algorithms were mainly variants of transformer architectures.

翻译：仇恨言论等攻击性在线内容的广泛性在网上引起了日益严重的社会问题。 AI工具对于支持在线平台的温和进程是必要的。为了评估这些识别工具,有必要继续试验不同语言的数据集。 HASOC 跟踪( HASOC 语音和攻击性内容识别) 专门为此开发基准数据。本文展示了英语、印地语和 Marathi 的HASOC 子轨迹。数据集来自 Twitter 。此子轨迹包含两个子任务。 A 任务是为所有三种语言提供二进制分类问题( 仇恨和非进攻性) 。 B 任务是三种语言的精细分类问题( HATE ) 、仇恨演讲、排斥性和普惠性。总体而言, 652 运行量由65个团队提交。任务 A 最佳分类算法的绩效分别为 F1 度 0.91 、 0.78 和 0.83, Marathi 、印地语和英文。本概述介绍了任务和数据开发以及详细结果。提交给竞争的系统应用了各种技术。。最佳演算法主要是变式结构。