针对印地语等印度语言，构建低误报率的暴力评论检测系统
Multi-Stage Training for Abusive Comment Detection in Indic Languages
- 分阶段训练+多模型集成，提升检测精度
- 显著降低误判率，保护言论自由
- 专为印地语等低资源语言设计，适合内容安全应用
近年来，社交媒体已成为重要的交流工具，人们借此分享观点、交换信息并展开讨论。鉴于其广泛普及和传播力，保障平台安全至关重要。社交媒体内容中存在大量暴力评论，因此检测此类内容变得日益关键。本文提出一种基于语言预处理的多模型集成方法，通过大量实验优化检测流程，显著降低误报率（将非暴力内容误判为暴力），使系统在有效识别暴力评论的同时，避免对正常表达造成干扰。
原文摘要 · Abstract (English)
In recent years social media has become an increasingly popular tool for communication. People use it to share their ideas, exchange information, and discuss thoughts. Given its prevalence and widespread reach, social media must remain a safe space for people. Content generated on social media can be abusive and it has become increasingly important to detect such content. In this paper, we use a language-based preprocessing and an ensemble of several models and analyze their performance of abusive comment detection. Through extensive experimentation, we propose a pipeline that minimizes the false-positive rate (marking non-abusive as abusive) so that these systems can detect abusive comments without undermining the freedom of expression.
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